{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ECCOv4 budgets\n",
    "Evaluating budgets in the ECCOv4 model run using xgcm. Currently, calculations are done only for one face (here `face = 1`).\n",
    "\n",
    "This notebook is based on the calculations and MATLAB code in evaluating_budgets_in_eccov4r3.pdf by Christopher G. Piecuch (ftp://ecco.jpl.nasa.gov/Version4/Release3/doc/evaluating_budgets_in_eccov4r3.pdf)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "\n",
    "from xmitgcm import open_mdsdataset\n",
    "import xgcm\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: Model output is saved into files with 4 different prefixes depending on whether the variable is averaged (`ave`) or a snapshot (`snp`) and whether it is 2D or 3D. Reference date and time step are defined to get appropriate time points. For this example the ECCOv4r2 solution has been run only for the first year (1992)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/rigel/home/jt2796/miniconda/envs/default/lib/python2.7/site-packages/xmitgcm-0.2.1-py2.7.egg/xmitgcm/utils.py:314: UserWarning: Not sure what to do with rlev = L\n",
      "  warnings.warn(\"Not sure what to do with rlev = \" + rlev)\n"
     ]
    }
   ],
   "source": [
    "ds_ave = open_mdsdataset('/rigel/ocp/users/jt2796/ECCO_v4_r2/run_budg3d_1yr/',\n",
    "                         delta_t=3600, ref_date='1991-12-15 12:0:0', geometry='llc',\n",
    "                         prefix=['monave2d','monave3d'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ds_snp = open_mdsdataset('/rigel/ocp/users/jt2796/ECCO_v4_r2/run_budg3d_1yr/',\n",
    "                         delta_t=3600, ref_date='1992-1-1 12:0:0', geometry='llc',\n",
    "                         prefix=['monsnp2d','monsnp3d'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adjust time axis (optional)\n",
    "**Note**: This code can be run to update the time axis to a certain common format. It is used here to be able to compare budget terms with other datasets (e.g., standard output from ECCOv4 netcdf files). Here, the time axis is defined for monthly averages to have time points always at the same day in the middle of the month (day=15)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ds_ave['time'] = pd.date_range(start='1992-01-15', periods=2*12, freq='SMS')[::2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Geothermal flux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "geoflx = np.fromfile('/rigel/ocp/users/jt2796/ECCO_v4_r2/geothermalFlux.bin', dtype=np.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: Geothermal flux dataset needs to be saved as an xarray data array with the same format as the model output. In order to reformat the loaded data array the byte-ordering needs to be changed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Data and type endianness don't match. Change data to match dtype and reshape to 1d\n",
    "geoflx = geoflx.byteswap().reshape([105300,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Reshape data for each face and save as xarray data array in LLC format\n",
    "geoflx00 = xr.DataArray(geoflx[:8100,0].reshape([90,90]),coords=[np.arange(0,90,1),np.arange(0,90,1)],\n",
    "                        dims=['j','i'])\n",
    "geoflx01 = xr.DataArray(geoflx[8100:16200,0].reshape([90,90]),coords=[np.arange(0,90,1),np.arange(0,90,1)],\n",
    "                        dims=['j','i'])\n",
    "geoflx02 = xr.DataArray(geoflx[16200:24300,0].reshape([90,90]),coords=[np.arange(0,90,1),np.arange(0,90,1)],\n",
    "                        dims=['j','i'])\n",
    "geoflx03 = xr.DataArray(geoflx[24300:32400,0].reshape([90,90]),coords=[np.arange(0,90,1),np.arange(0,90,1)],\n",
    "                        dims=['j','i'])\n",
    "geoflx04 = xr.DataArray(geoflx[32400:40500,0].reshape([90,90]),coords=[np.arange(0,90,1),np.arange(0,90,1)],\n",
    "                        dims=['j','i'])\n",
    "geoflx05 = xr.DataArray(geoflx[40500:48600,0].reshape([90,90]),coords=[np.arange(0,90,1),np.arange(0,90,1)],\n",
    "                        dims=['j','i'])\n",
    "geoflx06 = xr.DataArray(geoflx[48600:56700,0].reshape([90,90]),coords=[np.arange(0,90,1),np.arange(0,90,1)],\n",
    "                        dims=['j','i'])\n",
    "\n",
    "geoflx0709 = geoflx[56700:81000,0].reshape([90,270])\n",
    "geoflx07 = xr.DataArray(geoflx0709[:,:90],coords=[np.arange(0,90,1),np.arange(0,90,1)],dims=['j','i'])\n",
    "geoflx08 = xr.DataArray(geoflx0709[:,90:180],coords=[np.arange(0,90,1),np.arange(0,90,1)],dims=['j','i'])\n",
    "geoflx09 = xr.DataArray(geoflx0709[:,180:],coords=[np.arange(0,90,1),np.arange(0,90,1)],dims=['j','i'])\n",
    "\n",
    "geoflx1012 = geoflx[81000:,0].reshape([90,270])\n",
    "geoflx10 = xr.DataArray(geoflx1012[:,:90],coords=[np.arange(0,90,1),np.arange(0,90,1)],dims=['j','i'])\n",
    "geoflx11 = xr.DataArray(geoflx1012[:,90:180],coords=[np.arange(0,90,1),np.arange(0,90,1)],dims=['j','i'])\n",
    "geoflx12 = xr.DataArray(geoflx1012[:,180:],coords=[np.arange(0,90,1),np.arange(0,90,1)],dims=['j','i'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "geoflx_llc = xr.concat([geoflx00,geoflx01,geoflx02,geoflx03,geoflx04,geoflx05,geoflx06,\n",
    "                        geoflx07,geoflx08,geoflx09,geoflx10,geoflx11,geoflx12], 'face')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: Geothermal flux needs to be a three dimensional field since the sources are distributed along the ocean floor at various depths. This requires a three dimensional mask (see below)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define terms\n",
    "Before doing the budget calculations we need to define some terms that will be used in the budget calculations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of seconds between each snapshot\n",
    "**Note**: There are no snapshots for the first and last time point. Thus, we are skipping budget calculations for January and December 1992."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dt = ds_snp.time[1:].load()\n",
    "# delta t in seconds. Note: devide by 10**9 to convert nanoseconds to seconds\n",
    "dt.values = [float(t)/10**9 for t in np.diff(ds_snp.time)]\n",
    "\n",
    "# time axis of dt should be the same as of the monthly averages\n",
    "dt.time.values = ds_ave.time[1:-1].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relevant constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Density kg/m^3\n",
    "rhoconst = 1029\n",
    "\n",
    "# Heat capacity (J/kg/K)\n",
    "c_p = 3994\n",
    "\n",
    "# Constants for surface heat penetration (from Table 2 of Paulson and Simpson, 1977)\n",
    "R = 0.62\n",
    "zeta1 = 0.6\n",
    "zeta2 = 20.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ocean depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Ocean depth (m)\n",
    "Depth = ds_snp.sel(face=1).Depth.load()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grid dimensions\n",
    "**Note**: Only use one face for testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dxG = ds_ave.sel(face=1).dxG.load()\n",
    "dyG = ds_ave.sel(face=1).dyG.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rA = ds_ave.sel(face=1).rA.load()\n",
    "drF = ds_ave.sel(face=1).drF.load()\n",
    "hFacC = ds_ave.sel(face=1).hFacC.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Volume (m^3)\n",
    "vol = (rA*drF*hFacC).transpose('k','j','i')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Land mask "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Make copy of hFacC\n",
    "mskC = hFacC.copy(deep=True).load()\n",
    "\n",
    "# Change all fractions (ocean) to 1. land = 0\n",
    "mskC.values[mskC.values>0] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Make 2D land mask for surface (This is just for plotting/mapping purposes)\n",
    "land_mask = mskC[0]\n",
    "land_mask.values[land_mask.values==0] = np.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the grid object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grid = xgcm.Grid(ds_ave.sel(face=1), periodic=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating the volume budget\n",
    "$$G^{\\eta,tot} = G^{\\eta,conv} + G^{\\eta,forc}$$\n",
    "$$\\frac{1}{H}\\frac{\\partial \\eta}{\\partial t} = -\\nabla_{z^*}(s^*\\,{\\bf v}) - \\frac{\\partial w}{\\partial z^*} + s^*\\,F$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total tendency\n",
    "- ETAN: Surface Height Anomaly (m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load snapshots for surface height anomaly from dataset (here: only one face is used)\n",
    "ETANsnp = ds_snp.sel(face=1).ETAN.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/rigel/home/jt2796/miniconda/envs/default/lib/python2.7/site-packages/xarray/core/variable.py:1165: RuntimeWarning: divide by zero encountered in divide\n",
      "  else f(other_data, self_data))\n",
      "/rigel/home/jt2796/miniconda/envs/default/lib/python2.7/site-packages/xarray/core/variable.py:1164: RuntimeWarning: invalid value encountered in multiply\n",
      "  if not reflexive\n"
     ]
    }
   ],
   "source": [
    "# Total tendency (1/month)\n",
    "tendV_perMonth = (xr.DataArray(50*[1],coords={'k': np.array(range(0,50))},dims=['k'])*\\\n",
    "                  (1/Depth)*(ETANsnp.shift(time=-1)-ETANsnp)).transpose('time','k','j','i')[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Make sure time axis is the same as for the monthly variables\n",
    "tendV_perMonth.time.values = ds_ave.time[1:-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convert tendency from 1/month to 1/s\n",
    "tendV_perSec = tendV_perMonth/dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Predefine tendV array with correct dimensions\n",
    "tendV = xr.DataArray(np.nan*np.zeros([np.shape(tendV_perSec)[0]+2,50,90,90]),\n",
    "                     coords={'time': range(np.shape(tendV_perSec)[0]+2),'k': np.array(range(0,50)),\n",
    "                             'j': np.array(range(0,90)),'i': np.array(range(0,90))},dims=['time','k','j','i'])\n",
    "\n",
    "# Time\n",
    "tendV.time.values = ds_ave.time.values\n",
    "\n",
    "# Add coordinates\n",
    "tendV['XC'] = ds_snp.XC.sel(face=1)\n",
    "tendV['YC'] = ds_snp.YC.sel(face=1)\n",
    "tendV['Z'] = ds_snp.Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Total tendency (1/s)\n",
    "tendV.values[1:-1] = tendV_perSec.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forcing\n",
    "- oceFWflx: net surface Fresh-Water flux into the ocean (kg/m^2/s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load monthly averaged freshwater flux (here: only one face is used)\n",
    "oceFWflx = ds_ave.sel(face=1).oceFWflx.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/rigel/home/jt2796/miniconda/envs/default/lib/python2.7/site-packages/xarray/core/variable.py:1164: RuntimeWarning: divide by zero encountered in divide\n",
      "  if not reflexive\n",
      "/rigel/home/jt2796/miniconda/envs/default/lib/python2.7/site-packages/xarray/core/variable.py:1164: RuntimeWarning: invalid value encountered in divide\n",
      "  if not reflexive\n",
      "/rigel/home/jt2796/miniconda/envs/default/lib/python2.7/site-packages/ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in multiply\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "# Sea surface forcing on volume (1/s)\n",
    "forcV = ((oceFWflx/rhoconst)/(hFacC*drF)).transpose('time','k','j','i')\n",
    "\n",
    "# Make sure forcing term is zero below the surface\n",
    "forcV.values[:,1:] = 0*forcV.values[:,1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Horizontal convergence\n",
    "- UVELMASS: U Mass-Weighted Comp of Velocity (m/s)\n",
    "- VVELMASS: V Mass-Weighted Comp of Velocity (m/s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load monthly averaged velocities (here: only one face is used)\n",
    "UVELMASS = ds_ave.sel(face=1).UVELMASS.load()\n",
    "VVELMASS = ds_ave.sel(face=1).VVELMASS.load()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: Volume transports are calculated the same way as in the xgcm example (http://xgcm.readthedocs.io/en/latest/example_mitgcm.html#Divergence-Calculation). The only difference here is the omission of `hFacW` and `hFacS`. Including `hFacW` and `hFacS` in the calculation of the transport terms introduces unrealistic artifacts in the horizontal convergence near the ocean floor, which in turn causes the volume budget to be not balanced in the deeper ocean layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Horizontal volume transports (m^3/s)\n",
    "u_transport = UVELMASS * dyG * drF\n",
    "v_transport = VVELMASS * dxG * drF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convergence of the horizontal flow (1/s)\n",
    "hConvV = -(grid.diff(u_transport, 'X', boundary='extend') + \\\n",
    "           grid.diff(v_transport, 'Y', boundary='extend'))/vol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vertical convergence\n",
    "- WVELMASS: Vertical Mass-Weighted Comp of Velocity (m/s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load monthly averaged vertical velocity (here: only one face is used)\n",
    "WVELMASS = ds_ave.sel(face=1).WVELMASS.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Vertical volume transport (m^3/s)\n",
    "w_transport = WVELMASS * rA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: Apparently, it is required to add the vertical volume flux at the air-sea interface (`oceFWflx`) to the surface layer to balance the budget."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Add the vertical volume flux at the air-sea interface\n",
    "w_transport[:,0] = w_transport[:,0]+(forcV*vol)[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convergence of the vertical flow (m^3/s)\n",
    "vConvV = grid.diff(w_transport, 'Z', boundary='extend')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: Convergence in the deepest depth layer in `vConvV` needs to be replaced by minus the vertical volume flux. Otherwise, the volume budget in the deepest layer will be unbalanced. This is probably an issue with the given way `grid.diff()` calculates values at the edges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vConvV[:,-1,:,:] = -w_transport[:,-1,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convergence of the vertical flow (1/s)\n",
    "vConvV = vConvV/vol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total convergence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ConvV = hConvV+vConvV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total tendency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "totalV = ConvV + forcV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Map accumulated residual in volume budget "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2aab17f0ca10>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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R85BZ4Z5Gitg5RdKL5KnLSF4lxlOVsnUsQ1uEFMv4FnmbdcqTvY5FwrukcOd9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aab6d7eb810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "((totalV-tendV).sum(dim='k').sum(dim='time')*land_mask).plot(cmap='RdBu_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2aab6ff611d0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ITJghQzGvikJ47pyLtjZOR36CTZ3tdZe/rLM9Ue2tIdJc/gzDMIYIkS7C2FcAm7SNNcfs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\nHgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aab6e13e4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ignoring face boundaries\n",
    "((totalV-tendV).sum(dim='k').sum(dim='time')*land_mask)[1:89,1:89].plot(cmap='RdBu_r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: The residuals are larger when doing the calculations on Unix (on Habanero) compared to Mac OS. When running the code on Unix, there is some bias in the horizontal convergence term (`hConvV`), but it is very small."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time series for an arbitrarily chosen grid point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DyZMt3d0USCVLlmTkyJGMHTuWihUrUrduXaZOncqxY8e477770n3uhAkTuOGG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\nfPLJJ5w8eZIqVarQtWvXxP2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FBiN1KVHFu+0zBce+2bBuJMRFQqn6cNWcQjvDuDHGZIkI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jBlCjed+wX387zX3S8jmaOde0B6mEfU8sv48ZCUxEw6M4CnAZhWbiCNVw1WISUSTnFxXvETHc1o\nBtGeVznI2TTufRHb39uS9+NkZnpd9KpX97rs5VRImUGHDt66WbNUSPlAxZSIiJwZzj8fJk5kJA9T\nmW1spCYj18fDiBF+RxZ6s2dD374spDHdSQHg2RKD6bSyN1x4oc/BiRRC8fGQkkIUjuncRT2Ws8ud\nT6Om0ezd8P2J983M9P5NV6/utTZ98cWft4mKgjvv/L2Iqlo1NNchJ6ViSkREzhzt2lGyTROm0gOA\nJ3iUDcPnw9q1PgcWQu+/D126sMrVpg1vkEk0D0ePod8HzeHvf/c7OpHCq3NnGD2aYhxlHi2pzka2\nZv6NZrV3c3jHL3/ePjMTXn3VK6I6dIAtObRiRUVBx45eEfXKKyqiIoCKKRERObNMnEjdClu4lwlk\nUJRumS+Q3qUHpKX5HVnwrVkDd9zB5vQq3M47HKYEPSyFpAU1oFYtv6MTkYED4YEHKMOvvEtjKvI9\nqw5fQ6eaG8k8EBgIOzPTa12qVs1rbcqtiOrUyWulevll3SiJICqmRETkzBIbC8nJjGYQl/Bf1vEP\nRn/eBP71L78jC66vv4bGjdl+sBwNWcw+ytKCeUyeXhyLD9FzbUTk1JjBs89C27ZU5AcWEU8Z9jF3\nbz3aV1nLiLbrWV25o9fatHXrn/ePivJauLZsgZdegssvD/81yAlpND8RETkzdenC8pd2UJ/lFOUo\n/7Frqf7xFLjuOr8jK7gff4Trr2f39kPcyEq+pgo3k8qi0RspnviA39GJyPHS0rzfUaWmsoI63Mr7\nZBCDkUlx0lhGfWrzye/bZ7dEDR5cuB9A7iON5iciIoXbuHHccsGX3MNk0omhm5tKRtcecPiw35EV\nzL59EB/Pr9t/oRHv8TVVqMl63u73gQopkUhVrBi89RbUqMHNfEhr5gDgKMJRipJKXW+7qCjo0sVr\npZoxQ4XUaUDFlIiInJnKloWpU3mSRC7mO/5DHGO+aubd6T1dHTkCzZuTtnErLZnHWq6hMttY1HYa\nZZ4Z6nd0InIiZcrAe+9BpUo8wHMU4zBFSCeGdOqyArp2/b2I+tvf/I5W8kjd/ERE5MzWqxdLXthO\nQ5YQQxpruYYrV0yEOnX8juzUZGZCmzZkznub9szmTdpwHrtYVW8Ily2ZBNHRfkcoInmxdSvUrcvq\nny4llbrUjfqI2rP7Qps2fkcmx8hrNz8VUyIicmY7cACqV6fnd4N5kZ7U4lNWXdKJ6E3roFQpv6PL\nG+egd29ccjIJTGIKvSnNfj6s+QA1P54EJUr4HaGInIrDh2HqVNi5E5o2hdq1/Y5IjqNiSkREJNsH\nH7D/lhZUYzM7uIjRJJKYcBAmTvQ7srx57DEYPpzHGcowHqcYR1hSqRd11o6Fc8/1OzoRkTOOBqAQ\nERHJVq8eZfp05QV6AvAYw9g6aTksXepzYHkwYQIMH87z3McwHieKTF6LvY86K0eokBIR8ZmKKRER\nKRxGjiT+r9voRgppFKcb08jsdjfs3+93ZLl7/XXo04fZtOMBngPghZL9aP7hAKhY0efgRERExZSI\niBQOJUvC9Ok8wz+5gB/4hNqM3dEK+vf3O7KcLVsGnTqxxN1KF2biiGJU0SF0X9oRqlb1OzoREUHF\nlIiIFCY33MA5A+4mmV4APMoTfDVtJbzzjs+BHWftWmjZkk/Tr+YO5pJODA/asyS+df2Z8dBhEZEz\nhIopEREpXIYPp0nV/9KFGRzhLLqTQubd98DevX5H5vnmG2jUiC0HLqQJCzlEKTozkzEp5bDGjfyO\nTkREjqFiSkRECpfixWHmTMZG/ZO/sJNV3Mj4n9pAnz5+Rwa7dkHDhny/O4aGLOYXYmnMQqaO+pmo\nu7r4HZ2IiBwnKMWUmQ0wM2dmscE4noiISEjFxVH2kQQm0xuARxjBN7M+hTlz/Itp/35o1Ihfvt1H\nQxbzPRdzPat4o+8qig560L+4REQkVwUupszsIuA24P8KHo6IiEiYDBlCs5r/R0de5jAl6E4KWfck\nwO7d4Y/lyBFo0YJD67+iCQvZwhVUYxPvtH2JEs88Ef54REQkT4LRMvUskAiE/+m/IiIi+RUTAzNn\nMi56AOexi4+ow4Rf2kFCAoTzgfaZmdCpE0dTV9GKOXzKdVRiO4vqPUnZl8dDlHrki4hEqgK9Q5tZ\nM+AH59yGPGzby8zWmNmaPXv2FOS0IiIiwVGjBuWG9mESCQA8xCi2zV0Ps2aF5/zOwf33kzVnLt2Y\nxmLiiWUPS2omcuGCyVC0aHjiEBGRfDlpMWVmS81scw5Tc2Aw8FheTuScS3bOxTnn4sqXL1/QuEVE\nRIJj0CBaXvsD7XmV3yjJ3bxI1n19YOfO0J972DDc5Mn051lm0ZFSHOC9SglUWTbJey6WiIhEtJMW\nU865W51z1Y6fgG+BS4ENZrYdqAisNbPzQxuyiIhIEEVHw4wZjI8ZQHl2k0o9Ju9vDz17hra73+TJ\nMHQoI3iE5+hLDGm8FduTuA+fgXLlQndeEREJmnx383PObXLOVXDOXeKcuwTYAfzDObcraNGJiIiE\nQ9WqxI54kIncC0AiT7L93c9h2rTQnG/OHLj3XpLpyaMkYWTxcsne1E8dAhdfHJpziohI0OlXrSIi\nIgD9+tH6hl204XUOUYoeTMX17QfffRfc86Smwp13Mse1JIFJAEwo2p82S3rClVcG91wiIhJSQSum\nAi1UPwfreCIiImFVpAhMn87zZyUSyx6WU5/kgx2gRw/IygrOOdavh+bN+eDo9dzJLLIowr9sKAlz\nb4Prrw/OOUREJGzUMiUiIpLtr3+lwpiBPM/9AAzgKb5b9rX3+6aC+vZbiI9n7a+X0Zy3OUox7mc8\nQ6ZeArffXvDji4hI2KmYEhEROVZCAm3r/cwdzOEgZ9OTF3ADBsK2bfk/5k8/QYMGfP3T2cSziAOU\npj2vMm7kYazbXUELXUREwkvFlIiIyLGiorBpKUwsmci5/ML7NGDq4Q5w113eA3ZP1a+/QqNG7Nz2\nGw1Ywh4q0IDFzHhgLVGDBgY9fBERCR8VUyIiIserVInzxj7MePoA8E+e5vuV22HcuFM7TloatGzJ\nvnXfEs8itnMptfiUOW1fJ+bZ0WAW/NhFRCRsVEyJiIjkpEcPOsTvozlv8Stl6EUy7uFHYOvWvO2f\nmQmdO/Pb8tU0ZQGbqMHlbGVhvacp9dIkiNJHsIjI6U7v5CIiIjkxw6a+yKTSD1GWvSyiEdOPdoCu\nXSEj48T7Ogd9+5L+xjza8RoruYkL2cGSmonEzk+BmJjwXIOIiISUiikREZHcXHABf5nwKOPoC0B/\nnuWHz3bAk0+eeL+kJNyECfTkBd6hKWXZy5JL7uHipSlQqlQYAhcRkXBQMSUiInIiHTvSqflBbmcB\n+zmHe5iCe3wobNyY8/bJyTBkCIMYzQzuogSHWBh7F1ekToTY2LCGLiIioaViSkRE5ETMsCmTmVz2\nEcqwj4XczksZ7b3ufkeP/nHbefMgIYExDGAMiUSTzpsl76L2ByOgUiV/4hcRkZBRMSUiInIy553H\nhcmPM5Z+APRlHD+u3wVJSb9vs2IFdOjA9KzOJDIGgOlFe9FoUV+oVs2PqEVEJMRUTImIiORF69Z0\nbZdGI95lH2W97n5PJMGaNbBhAzRrxoK027ibFwEYa/3p+GZLuPFGnwMXEZFQUTElIiKSRzbheaaU\nH0Jp9rOAZszKagedOkF8PCt/rU5bXieTaAbzBH1frA7NmvkdsoiIhJCKKRERkbwqV46Lpg7lGR4E\n4AGeY9eX+9i4qzy38w5HOIueJDM8qQh07+5zsCIiEmoqpkRERE5F06Z075pFAxazl3I0ZT43sJL9\nnMMdzGFSny3Yww/5HaWIiISBiikREZFTZOPG8sL5j1GCQ6yhFgcpTRSZ9LnmY4qMfRrM/A5RRETC\nQMWUiIjIqSpThotnDKchi/63yMhiddkmEKWPVhGRwkLv+CIiIvnRoAEDex+iOIcpQjoxpFO3TXm/\noxIRkTCKLugBzKwPcD+QASx0ziUWOCoREZHTQO1JXVh+9SZS5/xC3VblqN2rut8hiYhIGBWomDKz\nekBzoIZzLs3MKgQnLBERkdND7V7Vqd3L7yhERMQPBe3mlwCMcs6lATjndhc8JBERERERkchX0GKq\nCnCTmX1qZivM7NrcNjSzXma2xszW7Nmzp4CnFRERERER8ddJu/mZ2VLg/BxWDQ7sXxa4DrgWeN3M\nKjvn3PEbO+eSgWSAuLi4P60XERERERE5nVgOdU/edzZbhNfNLzXw9zbgOufcCZuezGwP8F2+T1x4\nxAI/+x2ERAzlg+RGuSE5UV5ITpQXkhvlxh9Vcs6ddIjWgo7m9xZwC5BqZlWAGPLwPyEvgQmY2Rrn\nXJzfcUhkUD5IbpQbkhPlheREeSG5UW7kT0GLqRQgxcw2A0eBrjl18RMRERERETnTFKiYcs4dBToF\nKRYREREREZHTRkFH85PQSvY7AIkoygfJjXJDcqK8kJwoLyQ3yo18KNAAFCIiIiIiIoWVWqZERERE\nRETyQcWUiIiIiIhIPqiYCjIzSzGz3YERDrOX1TSz1Wa2ycwWmFnpwPIYM5sWWL7BzOoGlpcws4Vm\nttXMPjezUSc43zWB/b8xs+fMzALLxwT232hm88zsnBBfuuQgUvLhmPUDzMyZWWyILlnyIJLywsz6\nmNmXgWM8GcLLlpOIlLwws6vM7BMzW29ma8ysVogvXU7Ch9xIMrPvzezgccuLmdlrgZz51MwuCckF\nS55EUF48aGZfmPedc5mZVQrRJUcm55ymIE5AHeAfwOZjlv0buDkw3x0YHpi/D5gWmK8A/AevwC0B\n1AssjwE+Ahrlcr7PgNqAAe9lbwc0AKID86OB0X6/NoVxipR8CKy7CFiM98DsWL9fm8I8RUpeAPWA\npUCx7OP7/doU5imC8mLJMfONgVS/X5vCPvmQG9cBfwEOHrf8XmByYL498Jrfr01hniIoL+oBJQLz\nCYUtL9QyFWTOuQ+Bvcctvhz4MDD/PtAqMH8FsCyw325gHxDnnPvNOfdBYPlRYC1Q8fhzmdlfgNLO\nudXOy+CZQIvAfkuccxmBTT/JaX8JvUjJh4BngURAo874LILyIgEY5ZxLO+b44pMIygsHlA7MlwF2\nFvzqpCDCmRuB9Z84537MYVVzYEZg/k2g/vE9ICR8IiUvnHMfOOd+C/xZ6L5zqpgKj81As8B8G7wW\nAoANQHMzizazS4FrjlkHgHnd85oS+AdwnAuBHcf8vSOw7Hjd8e46SmQIez6YWTPgB+fchmBdhASd\nH+8TVYCbAt11VpjZtUG5EgkmP/KiHzDGzL4HngIeDsJ1SPCFKjdO5ELge4DADdv9QLl8RS+h4kde\nHKsHhew7p4qp8OgO3Gdm/wHOBo4GlqfgfYCtAcYCHwPZrUmYWTTwKvCcc+7bHI6b092gP7Q6mNng\nwDFfKeA1SPCENR/MrAQwGHgsaFcgoeDH+0Q0UBav68ZA4HXdZY44fuRFAtDfOXcR0B+YGoTrkOAL\nVW6cyEm/d4jv/MiL7GN0AuKAMfmO/jQU7XcAhYFzbiveb5gwsypAk8DyDLwPKgLrPga+PmbXZOBr\n59zYwPoieH1cAeYDk/hjU2pFjumOYWZdgduB+oFuHBIBfMiHy4BLgQ2B78kVgbVmVss5tyvY1yf5\n49P7xA5gbuD94TMzywJigT1BvTjJN5/yoivQNzD/BvBi8K5IgiVUueGcO9GNtx14rRk7Al++y/Dn\nbmbiI5/yAjO7Fe/G7c3ZXccLCxVTYWBmFZxzu80sCngUmBxYXgLvwcmHzOw2IMM590Vg3RN4b1J3\nZx/HOZcJXHXcsQ+Y2XXAp0AXYHxgeTwwCC+pf0MiRrjzwTm3Ce/HptnbbMfrJ/1zCC9TTpEf7xPA\nW8AtQGrgQzcGUF5EEJ/yYidwM5CKlx/HfuGSCBHK3DiB+XjF9mqgNbBcN2sjix95YWZXA1OA+EL5\n29uTjVCh6dQmvCbSH4F0vDs4PfDu8H0VmEbhJTPAJcCXwBa8EbUqBZZXxGs23/GQSyUAAADLSURB\nVAKsD0x353K+OLz+sduA54859jd4/Zqz95/s92tTGKdIyYfjttmORvNTXrj/jdz0cmDdWuAWv1+b\nwjxFUF7ciHdHegNeoXWN369NYZ98yI0nA+fJCvx3aGB5cbzWym/wRoOs7PdrU5inCMqLpcBPx+w/\n3+/XJpxT9gssIiIiIiIip0ADUIiIiIiIiOSDiikREREREZF8UDElIiIiIiKSDyqmRERERERE8kHF\nlIiIiIiISD6omBIREREREckHFVMiIiIiIiL58P98TKapGbhhAAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aabc3e62790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = 0\n",
    "j = 10\n",
    "i = 10\n",
    "\n",
    "f, axes = plt.subplots(2, 1,figsize=(12,5))\n",
    "f.tight_layout()\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(tendV.time, tendV[:,k,j,i], lw=4, color='blue', marker='.',label='total tendency')\n",
    "plt.plot(forcV.time, forcV[:,k,j,i], lw=2, color='red', marker='.',label='forcing')\n",
    "plt.plot(ConvV.time, ConvV[:,k,j,i], lw=2, color='orange', marker='.',label='convergence')\n",
    "plt.setp(plt.gca(), 'xticklabels',[])\n",
    "plt.legend(loc='upper right',frameon=False,fontsize=14)\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(totalV.time, totalV[:,k,j,i], lw=4, color='red', marker='.',label='RHS')\n",
    "plt.plot(tendV.time, tendV[:,k,j,i], lw=2, color='blue', marker='.',label='LHS')\n",
    "plt.plot(tendV.time, totalV[:,k,j,i]-tendV[:,k,j,i], lw=2, color='k', marker='.',label='RHS - LHS')\n",
    "plt.legend(loc='upper right',frameon=False,fontsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Verical profiles for an arbitrarily chosen grid point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vMCQMw+lNnrsU+C5QC4wJw/Cp+v3HAuOAYuDOMAxvrN+/F/AXYFfgNeA/wjCs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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aab6cfb5250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = 1\n",
    "j = 10\n",
    "i = 10\n",
    "\n",
    "f, axes = plt.subplots(1, 2, sharey=True, figsize=(12,7))\n",
    "f.tight_layout()\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(tendV[t,:,j,i],tendV.Z, lw=4, color='blue', marker='.',label='total tendency')\n",
    "plt.plot(forcV[t,:,j,i],forcV.Z, lw=2, color='red', marker='.',label='forcing')\n",
    "plt.plot(ConvV[t,:,j,i],ConvV.Z, lw=2, color='orange', marker='.',label='convergence')\n",
    "plt.legend(loc='lower right',frameon=False,fontsize=14)\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(totalV[t,:,j,i],totalV.Z, lw=4, color='red', marker='.',label='RHS')\n",
    "plt.plot(tendV[t,:,j,i],tendV.Z, lw=2, color='blue', marker='.',label='LHS')\n",
    "plt.plot(totalV[t,:,j,i]-tendV[t,:,j,i],totalV.Z, lw=2, color='k', marker='.',label='RHS - LHS')\n",
    "plt.setp(plt.gca(), 'yticklabels',[])\n",
    "plt.legend(loc='lower right',frameon=False,fontsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating the heat budget\n",
    "$$G^{\\theta,tot} = G^{\\theta,adv} + G^{\\theta,forc} + G^{\\theta,diff}$$\n",
    "$$\\frac{\\partial(s^*\\theta)}{\\partial t} = -\\nabla_{z^*}(s^*\\,\\theta\\,{\\bf v_{res}}) - \\frac{\\partial(\\theta\\,w_{res})}{\\partial z^*} + s^*\\,F_{\\theta} + s^*\\,D_{\\theta}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Total tendency\n",
    "- THETA: Potential Temperature (degC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load snapshots of theta (here only one face is used)\n",
    "THETAsnp = ds_snp.sel(face=1).THETA.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Calculate the s∗theta term\n",
    "HCsnp = THETAsnp*(1+ETANsnp/Depth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Total tendency (degC/month)\n",
    "tendH_perMonth = (HCsnp.shift(time=-1)-HCsnp)[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Make sure time axis is the same as for the monthly variables\n",
    "tendH_perMonth.time.values = ds_ave.time[1:-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convert tendency from 1/month to 1/s\n",
    "tendH_perSec = tendH_perMonth/dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Predefine tendH array with correct dimensions\n",
    "tendH = xr.DataArray(np.nan*np.zeros([np.shape(tendH_perSec)[0]+2,50,90,90]),\n",
    "                     coords={'time': range(np.shape(tendH_perSec)[0]+2),'k': np.array(range(0,50)),\n",
    "                             'j': np.array(range(0,90)),'i': np.array(range(0,90))},dims=['time','k','j','i'])\n",
    "\n",
    "# Time\n",
    "tendH.time.values = ds_ave.time.values\n",
    "\n",
    "# Add coordinates\n",
    "tendH['XC'] = ds_snp.XC.sel(face=1)\n",
    "tendH['YC'] = ds_snp.YC.sel(face=1)\n",
    "tendH['Z'] = ds_snp.Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Total tendency (degC/s)\n",
    "tendH.values[1:-1] = tendH_perSec.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forcing\n",
    "- TFLUX: total heat flux (match heat-content variations) (W/m^2)\n",
    "- oceQsw: net Short-Wave radiation (+=down) (W/m^2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load monthly averages of heat flux and shortwave radiation (here only one face is used)\n",
    "TFLUX = ds_ave.sel(face=1).TFLUX.load()\n",
    "oceQsw = ds_ave.sel(face=1).oceQsw.load()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Defining terms needed for evaluating surface heat forcing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Z = ds_ave.sel(face=1).Z.load()\n",
    "RF = np.concatenate([ds_ave.sel(face=1).Zp1.values[:-1],[np.nan]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: `Z` and `Zp1` are used in deriving surface heat penetration. MATLAB code uses `RF` from `mygrid` structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "q1 = R*np.exp(1.0/zeta1*RF[:-1]) + (1.0-R)*np.exp(1.0/zeta2*RF[:-1])\n",
    "q2 = R*np.exp(1.0/zeta1*RF[1:]) + (1.0-R)*np.exp(1.0/zeta2*RF[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Correction for the 200m cutoff\n",
    "zCut = np.where(Z < -200)[0][0]\n",
    "q1[zCut:] = 0\n",
    "q2[zCut-1:] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Save q1 and q2 as xarray data arrays\n",
    "q1 = xr.DataArray(q1,coords=[Z.k],dims=['k'])\n",
    "q2 = xr.DataArray(q2,coords=[Z.k],dims=['k'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute vertically penetrating flux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Surface heat flux (below the surface)\n",
    "forcH = ((q1*(mskC==1)-q2*(mskC.shift(k=-1)==1))*oceQsw).transpose('time','k','j','i')\n",
    "\n",
    "# Reset surface layer to zero\n",
    "forcH.values[:,0] = 0*forcH.values[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Surface heat flux (at the sea surface)\n",
    "forcH[:,0] = ((TFLUX - (1-(q1[0]-q2[0]))*oceQsw)*mskC[0]).transpose('time','j','i')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add geothermal heat flux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create 3d bathymetry mask\n",
    "mskC_shifted = mskC.shift(k=-1)\n",
    "mskC_shifted.values[-1,:,:] = 0\n",
    "mskb = mskC - mskC_shifted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create 3d field of geothermal heat flux\n",
    "geoflx2d = geoflx_llc.sel(face=1)\n",
    "geoflx3d = geoflx2d * mskb\n",
    "GEOFLX = geoflx3d.transpose('k','j','i')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Add geothermal heat flux to forcing field and convert from W/m^2 to degC/s\n",
    "forcH = ((forcH + GEOFLX)/(rhoconst*c_p))/(hFacC*drF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Advection\n",
    "#### Horizontal convergence\n",
    "- ADVx_TH: U Comp. Advective Flux of Pot.Temperature (degC m^3/s)\n",
    "- ADVy_TH: V Comp. Advective Flux of Pot.Temperature (degC m^3/s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load monthly averaged advective fluxes (here only one face is used)\n",
    "ADVx_TH = ds_ave.sel(face=1).ADVx_TH.load()\n",
    "ADVy_TH = ds_ave.sel(face=1).ADVy_TH.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convergence of horizontal advection (degC/s)\n",
    "adv_hConvH = -(grid.diff(ADVx_TH, 'X', boundary='extend') + \\\n",
    "               grid.diff(ADVy_TH, 'Y', boundary='extend'))/vol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Vertical convergence\n",
    "- ADVr_TH: Vertical Advective Flux of Pot.Temperature (degC m^3/s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load monthly averages of vertical advective flux (here only one face is used)\n",
    "ADVr_TH = ds_ave.sel(face=1).ADVr_TH.load()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: The heat budget only balances when the sea surface forcing is not added to the vertical advective flux (at the air-sea interface). This is different from the volume and salinity budget."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convergence of the vertical advection (degC m^3/s)\n",
    "adv_vConvH = grid.diff(ADVr_TH, 'Z', boundary='extend')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: Convergence in the deepest layer in `adv_vConvH` needs to be replaced by minus the vertical advective flux. Otherwise, the volume budget in the deepest layer will be unbalanced. This is probably an issue with the given way `grid.diff()` calculates values at the edges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "adv_vConvH[:,-1,:,:] = -ADVr_TH[:,-1,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convergence of vertical advection (degC/s)\n",
    "adv_vConvH = adv_vConvH/vol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Diffusion\n",
    "#### Horizontal convergence\n",
    "- DFxE_TH: U Comp. Diffusive Flux of Pot.Temperature (degC m^3/s)\n",
    "- DFyE_TH: V Comp. Diffusive Flux of Pot.Temperature (degC m^3/s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load monthly averages of diffusive fluxes (here only one face is used)\n",
    "DFxE_TH = ds_ave.sel(face=1).DFxE_TH.load()\n",
    "DFyE_TH = ds_ave.sel(face=1).DFyE_TH.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convergence of horizontal diffusion (degC/s)\n",
    "dif_hConvH = -(grid.diff(DFxE_TH, 'X', boundary='extend') + \\\n",
    "               grid.diff(DFyE_TH, 'Y', boundary='extend'))/vol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Vertical convergence\n",
    "- DFrE_TH: Vertical Diffusive Flux of Pot.Temperature (Explicit part) (degC m^3/s)\n",
    "- DFrI_TH: Vertical Diffusive Flux of Pot.Temperature (Implicit part) (degC m^3/s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load monthly averages of vertical diffusive fluxes (here only one face is used)\n",
    "DFrE_TH = ds_ave.sel(face=1).DFrE_TH.load()\n",
    "DFrI_TH = ds_ave.sel(face=1).DFrI_TH.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convergence of vertical diffusion (degC m^3/s)\n",
    "dif_vConvH = grid.diff(DFrE_TH, 'Z', boundary='extend') + grid.diff(DFrI_TH, 'Z', boundary='extend')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: Convergence in the deepest layer in `dif_vConvH` needs to be replaced by minus the vertical diffusive flux to balance the budget."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dif_vConvH[:,-1,:,:] = -(DFrE_TH+DFrI_TH)[:,-1,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convergence of vertical diffusion (degC/s)\n",
    "dif_vConvH = dif_vConvH/vol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total convergences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Total convergence of advective flux\n",
    "adv_ConvH = adv_hConvH + adv_vConvH\n",
    "\n",
    "# Total convergence of diffusive flux\n",
    "dif_ConvH = dif_hConvH + dif_vConvH\n",
    "\n",
    "# Total convergence\n",
    "ConvH = adv_ConvH + dif_ConvH"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total tendency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "totalH = ConvH + forcH"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Map accumulated residual in volume budget "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2aac43f29350>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cYGZrJJ0JrDazlcD5wDclraUYESwPZddIupQiwOdm4EPBkwhJ36VYJGyupBHg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aadc3f86750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "((totalH-tendH).sum(dim='k').sum(dim='time')*land_mask).plot(cmap='RdBu_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2aab1804b8d0>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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L5iDFqfj+i1QUYrJp5qpjWQv329mwIhK5ghXtUArZq2ZMYr1syeqB/ICYHn1i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\nEDSMWNiDIAgaxv8DYarZsrPCzMsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aac43e34510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ignoring face boundaries\n",
    "((totalH-tendH).sum(dim='k').sum(dim='time')*land_mask)[1:89,1:89].plot(cmap='RdBu_r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time series for an arbitrarily chosen grid point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ClKEv580HspVS0xvyGAlTQpzD2bOwY4dtwKqtNzcwUIeqSy7RXwcPBh+flm+v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\nkQVSTAkhhBBCCCFEFvw/ycwx7eFhLDMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aac43e79f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = 1\n",
    "j = 10\n",
    "i = 10\n",
    "\n",
    "f, axes = plt.subplots(2, 1,figsize=(12,5))\n",
    "f.tight_layout()\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(tendH.time, tendH[:,k,j,i], lw=4, color='blue', marker='.',label='total tendency')\n",
    "plt.plot(forcH.time, forcH[:,k,j,i], lw=2, color='red', marker='.',label='forcing')\n",
    "plt.plot(adv_ConvH.time, adv_ConvH[:,k,j,i], lw=2, color='orange', marker='.',label='advection')\n",
    "plt.plot(dif_ConvH.time, dif_ConvH[:,k,j,i], lw=2, color='purple', marker='.',label='diffusion')\n",
    "plt.setp(plt.gca(), 'xticklabels',[])\n",
    "plt.legend(loc='upper center',frameon=False,fontsize=14)\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(totalH.time, totalH[:,k,j,i], lw=4, color='red', marker='.',label='RHS')\n",
    "plt.plot(tendH.time, tendH[:,k,j,i], lw=2, color='blue', marker='.',label='LHS')\n",
    "plt.plot(tendH.time, totalH[:,k,j,i]-tendH[:,k,j,i], lw=2, color='k', marker='.',label='RHS - LHS')\n",
    "plt.legend(loc='upper center',frameon=False,fontsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Verical profiles for an arbitrarily chosen grid point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fyj4chQ46RXQiMCyw1nGvtnxt3AizZ1vb8+ZBWJgHby7SjjgcAJTgTwoxDOUH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\nIiIiteD/BwXWP84CluK3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aac43e79110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = 1\n",
    "j = 10\n",
    "i = 10\n",
    "\n",
    "f, axes = plt.subplots(1, 2, sharey=True, figsize=(12,7))\n",
    "f.tight_layout()\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(tendH[t,:,j,i],tendH.Z, lw=4, color='blue', marker='.',label='total tendency')\n",
    "plt.plot(forcH[t,:,j,i],forcH.Z, lw=2, color='red', marker='.',label='forcing')\n",
    "plt.plot(adv_ConvH[t,:,j,i],adv_ConvH.Z, lw=2, color='orange', marker='.',label='advection')\n",
    "plt.plot(dif_ConvH[t,:,j,i],dif_ConvH.Z, lw=2, color='purple', marker='.',label='diffusion')\n",
    "plt.legend(loc='lower left',frameon=False,fontsize=14)\n",
    "plt.ylim([-200,0])\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(totalH[t,:,j,i],totalH.Z, lw=4, color='red', marker='.',label='RHS')\n",
    "plt.plot(tendH[t,:,j,i],tendH.Z, lw=2, color='blue', marker='.',label='LHS')\n",
    "plt.plot(totalH[t,:,j,i]-tendH[t,:,j,i],tendH.Z, lw=2, color='k', marker='.',label='RHS - LHS')\n",
    "plt.setp(plt.gca(), 'yticklabels',[])\n",
    "plt.legend(loc='lower right',frameon=False,fontsize=14)\n",
    "plt.ylim([-200,0])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating the salt budget\n",
    "$$G^{S,tot} = G^{S,adv} + G^{S,forc} + G^{S,diff}$$\n",
    "$$\\frac{\\partial(s^*S)}{\\partial t} = -\\nabla_{z^*}(s^*\\,S\\,{\\bf v_{res}}) - \\frac{\\partial(S\\,w_{res})}{\\partial z^*} + s^*\\,F_S + s^*\\,D_S$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total tendency\n",
    "- SALT: Salinity (psu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load salinity snapshot (here only one face is used)\n",
    "SALTsnp = ds_snp.sel(face=1).SALT.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Calculate s*S term\n",
    "sSALT = SALTsnp*(1+ETANsnp/Depth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Total tendency (psu/month)\n",
    "tendS_perMonth = (sSALT.shift(time=-1)-sSALT)[:-1]x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Make sure time axis is the same as for the monthly variables\n",
    "tendS_perMonth.time.values = ds_ave.time[1:-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convert tendency from 1/month to 1/s\n",
    "tendS_perSec = tendS_perMonth/dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Predefine tendS array with correct dimensions\n",
    "tendS = xr.DataArray(np.nan*np.zeros([np.shape(tendS_perSec)[0]+2,50,90,90]),\n",
    "                     coords={'time': range(np.shape(tendS_perSec)[0]+2),'k': np.array(range(0,50)),\n",
    "                             'j': np.array(range(0,90)),'i': np.array(range(0,90))},dims=['time','k','j','i'])\n",
    "\n",
    "# Time\n",
    "tendS.time.values = ds_ave.time.values\n",
    "\n",
    "# Add coordinates\n",
    "tendS['XC'] = ds_snp.XC.sel(face=1)\n",
    "tendS['YC'] = ds_snp.YC.sel(face=1)\n",
    "tendS['Z'] = ds_snp.Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Total tendency (psu/s)\n",
    "tendS.values[1:-1] = tendS_perSec.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forcing\n",
    "- SFLUX: total salt flux (match salt-content variations) (g/m^2/s)\n",
    "- oceSPtnd: salt tendency due to salt plume flux (g/m^2/s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load monthly averaged SFLUX and oceSPtnd (here only one face is used)\n",
    "SFLUX = ds_ave.sel(face=1).SFLUX.load()\n",
    "oceSPtnd = ds_ave.sel(face=1).oceSPtnd.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Expand SFLUX along depth axis\n",
    "SFLUX3d = xr.concat(50*[SFLUX.expand_dims('k',1)],dim='k')\n",
    "SFLUX3d.coords['k'] = SFLUX3d.k\n",
    "\n",
    "# Reset SFLUX3d to zero below surface layer\n",
    "SFLUX3d.values[:,1:] = 0*SFLUX3d[:,1:].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: `SFLUX` and `oceSPtnd` is given in g/m^2/s. Dividing by density and corresponding vertical length scale (`drF`) results in g/kg/s, which is the same as psu/s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Surface salt flux (psu/s)\n",
    "forcS = (((SFLUX3d+oceSPtnd)/rhoconst)/(hFacC*drF)).transpose('time','k','j','i')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Advection\n",
    "#### Horizontal convergence\n",
    "- ADVx_SLT: U Comp. Advective Flux of Salinity (psu m^3/s)\n",
    "- ADVy_SLT: V Comp. Advective Flux of Salinity (psu m^3/s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load monthly averaged advective fluxes (here only one face is used)\n",
    "ADVx_SLT = ds_ave.sel(face=1).ADVx_SLT.load()\n",
    "ADVy_SLT = ds_ave.sel(face=1).ADVy_SLT.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convergence of horizontal advection (psu/s)\n",
    "adv_hConvS = -(grid.diff(ADVx_SLT, 'X', boundary='extend') + \\\n",
    "               grid.diff(ADVy_SLT, 'Y', boundary='extend'))/vol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Vertical convergence\n",
    "- ADVr_SLT: Vertical Advective Flux of Salinity (psu m^3/s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load monthly averaged vertical advective flux (here only one face is used)\n",
    "ADVr_SLT = ds_ave.sel(face=1).ADVr_SLT.load()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: The salt budget only balances when the sea surface forcing is not added to the vertical salt flux (at the air-sea interface). This is different from the volume and salinity budget."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convergence of vertical advection (psu m^3/s)\n",
    "adv_vConvS = grid.diff(ADVr_SLT, 'Z', boundary='extend')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: The deepest depth layer in `adv_vConvS` needs to be replaced by  minus the vertical advective flux. This is probably an issue with how `grid.diff()` calculates the edges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "adv_vConvS[:,-1,:,:] = -ADVr_SLT[:,-1,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convergence of vertical advection (psu/s)\n",
    "adv_vConvS = adv_vConvS/vol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Diffusion\n",
    "#### Horizontal convergence\n",
    "- DFxE_SLT: U Comp. Diffusive Flux of Salinity (psu m^3/s)\n",
    "- DFyE_SLT: V Comp. Diffusive Flux of Salinity (psu m^3/s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load monthly averaged horizontal diffusive fluxes (here only one face is used)\n",
    "DFxE_SLT = ds_ave.sel(face=1).DFxE_SLT.load()\n",
    "DFyE_SLT = ds_ave.sel(face=1).DFyE_SLT.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convergence of horizontal diffusion (psu/s)\n",
    "dif_hConvS = -(grid.diff(DFxE_SLT, 'X', boundary='extend') + \\\n",
    "               grid.diff(DFyE_SLT, 'Y', boundary='extend'))/vol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Vertical convergence\n",
    "- DFrE_SLT: Vertical Diffusive Flux of Salinity (Explicit part) (psu m^3/s)\n",
    "- DFrI_SLT: Vertical Diffusive Flux of Salinity (Implicit part) (psu m^3/s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load monthly averaged vertical diffusive fluxes (here only one face is used)\n",
    "DFrE_SLT = ds_ave.sel(face=1).DFrE_SLT.load()\n",
    "DFrI_SLT = ds_ave.sel(face=1).DFrI_SLT.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convergence of vertical diffusion (psu m^3/s)\n",
    "dif_vConvS = grid.diff(DFrE_SLT, 'Z', boundary='extend') + grid.diff(DFrI_SLT, 'Z', boundary='extend')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: The deepest depth layer in `dif_vConvS` needs to be calculated seperately. This is probably an issue with the given boundary condition in `grid.diff()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convergence of vertical diffusion (psu/s)\n",
    "dif_vConvS = dif_vConvS/vol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total convergences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Total convergence of advective flux\n",
    "adv_ConvS = adv_hConvS + adv_vConvS\n",
    "\n",
    "# Total convergence of diffusive flux\n",
    "dif_ConvS = dif_hConvS + dif_vConvS\n",
    "\n",
    "# Total convergence\n",
    "ConvS = adv_ConvS + dif_ConvS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total tendency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "totalS = ConvS + forcS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot accumulated residual in salt budget "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2aae5e3aaed0>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TpFtKmz4cZGatXXrWAwd2KihpZWvDiI0bNmQW03Ecp4XPDHJwvJn9XNKBwHWS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aae5e9be490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "((totalS-tendS).sum(dim='k').sum(dim='time')*land_mask).plot(cmap='RdBu_r')#,vmin=-5e-10,vmax=5e-10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2aabc3ef0090>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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h4bFZMG8e8fDw8NhGMFyZjkgzOwTgfbJpP4BfBfCXre2TAA4DeJVzbm7l/h1w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\nou1wOBwDBF+0HQ6HY4Dgi7bD4XAMEP4//6KmhfTo++cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aadc3ed8890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ignoring face boundaries\n",
    "((totalS-tendS).sum(dim='k').sum(dim='time')*land_mask)[1:89,1:89].plot(cmap='RdBu_r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time series for an arbitrarily chosen grid point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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E4aNL2zo8IYoESaZsxRQPx76Cw9MgMVovKx2gJ1Vln5IfVEIUJZm1QnnUxvxI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\nVIiIiJTA/wNipZRwRlNrhwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aad43fc3650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = -1\n",
    "j = 3\n",
    "i = 11\n",
    "\n",
    "f, axes = plt.subplots(2, 1,figsize=(12,5))\n",
    "f.tight_layout()\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(tendS.time, tendS[:,k,j,i], lw=4, color='blue', marker='.',label='total tendency')\n",
    "plt.plot(forcS.time, forcS[:,k,j,i], lw=2, color='red', marker='.',label='forcing')\n",
    "plt.plot(adv_ConvS.time, adv_ConvS[:,k,j,i], lw=2, color='orange', marker='.',label='advection')\n",
    "plt.plot(dif_ConvS.time, dif_ConvS[:,k,j,i], lw=2, color='purple', marker='.',label='diffusion')\n",
    "plt.setp(plt.gca(), 'xticklabels',[])\n",
    "plt.legend(loc='upper center',frameon=False,fontsize=14)\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "#plt.axhline(y=0, xmin=0, xmax=1, linewidth=0.5, color = 'k')\n",
    "plt.plot(totalS.time, totalS[:,k,j,i], lw=4, color='red', marker='.',label='RHS')\n",
    "plt.plot(tendS.time, tendS[:,k,j,i], lw=2, color='blue', marker='.',label='LHS')\n",
    "plt.plot(tendS.time, totalS[:,k,j,i]-tendS[:,k,j,i], lw=2, color='k', marker='.',label='RHS - LHS')\n",
    "plt.legend(loc='upper center',frameon=False,fontsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Verical profiles for an arbitrarily chosen grid point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YJCaSzWKnmy++gJHDAiybuYyvH/+avev2VrysPcHOlZ9fqWAmTZZCmYicMEwT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\nRHykpExERERERMRH/x9OL+kr+uJ/JwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aab6d31ef10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = 1\n",
    "j = 10\n",
    "i = 10\n",
    "\n",
    "f, axes = plt.subplots(1, 2, sharey=True, figsize=(12,7))\n",
    "f.tight_layout()\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(tendS[t,:,j,i], tendS.Z, lw=4, color='blue', marker='.',label='total tendency')\n",
    "plt.plot(forcS[t,:,j,i], forcS.Z, lw=2, color='red', marker='.',label='forcing')\n",
    "plt.plot(adv_ConvS[t,:,j,i], adv_ConvS.Z, lw=2, color='orange', marker='.',label='advection')\n",
    "plt.plot(dif_ConvS[t,:,j,i], dif_ConvS.Z, lw=2, color='purple', marker='.',label='diffusion')\n",
    "plt.legend(loc='lower left', frameon=False, fontsize=14)\n",
    "plt.ylim([-1000,0])\n",
    "#plt.xlim([-2e-10,2e-10])\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.axvline(x=0, ymin=0, ymax=1, linewidth=0.5, color = 'k')\n",
    "plt.plot(totalS[t,:,j,i], totalS.Z, lw=4, color='red', marker='.',label='RHS')\n",
    "plt.plot(tendS[t,:,j,i], tendS.Z, lw=2, color='blue', marker='.',label='LHS')\n",
    "plt.plot(totalS[t,:,j,i]-tendS[t,:,j,i], tendS.Z, lw=2, color='k', marker='.',label='RHS - LHS')\n",
    "plt.setp(plt.gca(), 'yticklabels',[])\n",
    "plt.legend(loc='lower left',frameon=False,fontsize=14)\n",
    "plt.ylim([-1000,0])\n",
    "#plt.xlim([-2e-10,2e-10])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating the salinity budget\n",
    "$$G^{Sln,tot} = G^{Sln,adv} + G^{Sln,forc} + G^{Sln,diff}$$\n",
    "$$\\frac{\\partial S}{\\partial t} = \\frac{1}{s^*} \\left[S\\,\\nabla_{z^*}(s^*\\,{\\bf v}) + S\\,\\frac{\\partial w}{\\partial z^*} - \\nabla_{z^*}(s^*\\,S\\,{\\bf v_{res}}) - \\frac{\\partial S\\,w_{res}}{\\partial z^*}\\right] + D_S + F_S - S\\,F$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scale factor\n",
    "- Depth: Ocean_depth (m)\n",
    "- ETAN: Surface Height Anomaly (m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load monthly averaged surface height anomaly (here only one face is used)\n",
    "ETAN = ds_ave.sel(face=1).ETAN.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Scale factor\n",
    "rstarfac = ((Depth + ETAN)/Depth).transpose('time','j','i')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total tendency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Total tendency (psu/month)\n",
    "tendSln_perMonth = (SALTsnp.shift(time=-1)-SALTsnp)[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Make sure time axis is the same as for the monthly variables\n",
    "tendSln_perMonth.time.values = ds_ave.time[1:-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Total tendency (psu/s)\n",
    "tendSln_perSec = tendSln_perMonth/dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Predefine tendSln array with correct dimensions\n",
    "tendSln = xr.DataArray(np.nan*np.zeros([np.shape(tendSln_perSec)[0]+2,50,90,90]),\n",
    "                       coords={'time': range(np.shape(tendSln_perSec)[0]+2),'k': np.array(range(0,50)),\n",
    "                               'j': np.array(range(0,90)),'i': np.array(range(0,90))},dims=['time','k','j','i'])\n",
    "\n",
    "# Time\n",
    "tendSln.time.values = ds_ave.time.values\n",
    "\n",
    "# Add coordinates\n",
    "tendSln['XC'] = ds_snp.XC.sel(face=1)\n",
    "tendSln['YC'] = ds_snp.YC.sel(face=1)\n",
    "tendSln['Z'] = ds_snp.Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Total tendency (psu/s)\n",
    "tendSln.values[1:-1] = tendSln_perSec.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forcing\n",
    "**Note**: The forcing term is comprised of both salt flux (`forcS`) and volume (i.e., freshwater) flux (`forcV`).\n",
    "- SALT: Salinity (psu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load monthly averaged salinity fields (here only one face is used)\n",
    "SALT = ds_ave.sel(face=1).SALT.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Sea surface forcing for salinity (psu/s)\n",
    "forcSln = (-SALT*forcV + forcS)/rstarfac"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Advection\n",
    "#### Horizontal convergence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "adv_hConvSln = (-SALT*hConvV + adv_hConvS)/rstarfac"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Vertical convergence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "adv_vConvSln = (-SALT*vConvV + adv_vConvS)/rstarfac"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Diffusion\n",
    "#### Horizontal convergence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dif_hConvSln = dif_hConvS/rstarfac"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Vertical convergence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dif_vConvSln = dif_vConvS/rstarfac"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total convergences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Total convergence of advective flux\n",
    "adv_ConvSln = adv_hConvSln + adv_vConvSln\n",
    "\n",
    "# Total convergence of diffusive flux\n",
    "dif_ConvSln = dif_hConvSln + dif_vConvSln\n",
    "\n",
    "# Total convergence\n",
    "ConvSln = adv_ConvSln + dif_ConvSln"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total tendency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "totalSln = ConvSln + forcSln"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Accumulated residual in salinity budget "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2aae75c5c750>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OMbNVZrZqyR575FLTcZwRMW69ja0ITcVtUdKkYyXdImmdpNN77N9e0gVh/9WSVpT2nRHK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\nxo2H4ziOU5v/C6r1NF5pVb95AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aae5ec6fb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "((totalSln-tendSln).sum(dim='k').sum(dim='time')*land_mask).plot(cmap='RdBu_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2aae5f3b2f10>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2V7894XLPMYmTkI2KfXsjUmXEtN3XQMQeEzFVmNCInjO3A8oAw8dLZMTjrCyc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\nB+O2rfkMKXR5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aae5ecd7310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ignoring face boundaries\n",
    "((totalSln-tendSln).sum(dim='k').sum(dim='time')*land_mask)[1:89,1:89].plot(cmap='RdBu_r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time series for an arbitrarily chosen grid point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Uffv28fnnn/Pyyy/j7+9/1vfx9/fngQceYPbs2Xh5eTFmzBiOHj3K5s2b3bpE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aae5ee37190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = 0\n",
    "j = 0\n",
    "i = 10\n",
    "\n",
    "f, axes = plt.subplots(2, 1,figsize=(12,5))\n",
    "f.tight_layout()\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(tendSln.time, tendSln[:,k,j,i], lw=4, color='blue', marker='.',label='total tendency')\n",
    "plt.plot(forcSln.time, forcSln[:,k,j,i], lw=2, color='red', marker='.',label='forcing')\n",
    "plt.plot(adv_ConvSln.time, adv_ConvSln[:,k,j,i], lw=2, color='orange', marker='.',label='advection')\n",
    "plt.plot(dif_ConvSln.time, dif_ConvSln[:,k,j,i], lw=2, color='purple', marker='.',label='diffusion')\n",
    "plt.setp(plt.gca(), 'xticklabels',[])\n",
    "plt.legend(loc='lower right',frameon=False,fontsize=14)\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(totalSln.time, totalSln[:,k,j,i], lw=4, color='red', marker='.',label='RHS')\n",
    "plt.plot(tendSln.time, tendSln[:,k,j,i], lw=2, color='blue', marker='.',label='LHS')\n",
    "plt.plot(tendSln.time, totalSln[:,k,j,i]-tendSln[:,k,j,i], lw=2, color='k', marker='.',label='RHS - LHS')\n",
    "plt.legend(loc='upper left',frameon=False,fontsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Verical profiles for an arbitrarily chosen grid point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ECQlhv53O5yPG6SSmSTV8hQ0PjVWmg3BWazBZhpmVjOEe/oGbKPT4uJF3iMFZ\nI3IdJZlS4igljjx6Nug1RrudJB0oJPlAbVdbW2NfvFKMLqljwwJccjLWiRMD9zIYDNKnLMSk+aIQ\nIeAr3oNufi/K3GZe/OaPLFidxfJdNb89WsFoyokhb9KN9J9uYecn69j17a6KVo3Ep8cz4qYRjLhp\nBB1SWqajtRBnKmm+KISIGK9Xa1ZTS4Czz92HbaEP65BCLANOwNGjNRb1yFGKPTG1hrVCkmrdX0Zs\no4qoxxPUZLJ688nqSyLH6ISXE8D8AQOY0q9fnSGO5GStH91ZTvqUCdGS1twH21+GzpNxTsjmww/h\n5Zdh06bgw/aQRjr5XMuHfG74JX/4Azx4axHr313PurfXUbS3SDtQgT4X9SF1YiqqqtIzqycplpSa\n9xVCnJaEMiFEm6WqWmf3WgIbhYW17i8rU+uIVDWXxo6yqeBDJQk4wUD+zAD6BV2xeqjbTS+WdZyK\n9Z5hWJ68MDw/pzZAQpkQLeXQD7DwQkAFnRGybJBsQVW1+aFfegkWLIDx2FnKBHSoODAxmYUsx8J3\n38EFF4DqU8nNzmXdW+vY9t9teF2VI4zoo/XMWDRDgpkQjSChTAhxVikra1SIKy9ycIzEBoe4Y2wH\nMtCa95iAhUBdfcpUFFRMOMl+4GssL0xriZ9CqyMDfQjREpxHwX4jgfaHPi8csUGyBUWBrCxt2bED\ntk63oazQjjPgxoqN5Vj48EMtlCk6hd4X9Kb3Bb0pO1bGFzO+YOcCbZx+b7mXnD/lcN2C61DOsHlP\nhBBCCBECZjOkpWlLA0S7XHQrLKRbbUHu6PoaYe7ZwuM85v+8o8PJNdzKRDJqbVa5h3RKaIeKDgcm\nvn1xM5ZR5XDddeH8CbRpEsqEaKqSPFh0ETj2AwqgA70ROllrHNqvH/R7yYo6QZs82o0BG9pxUbX8\nLzQnmsl4NIM9C/fgcXpAhV1f7+LTn3/KFe9cgSneFMYXJoQQQogzntEI3bppSwNMXrIkMES10Wjk\nt09ci6VjR39o2w6FSwMBzr6nC5NOfUk50YCOj9Rr+c31k+m+Zw888sgZN7F2KEgoE6IpTm72B7ID\nED8chj0DRZu0QJZ8mqp8iwUlJQXy87mJt1nur/Lfv7/2w1MsKUzPnk6eLQ9Fp7Dkz0vY9sU2Dm88\nzC/m/oKuo7qG57UJIYQQQlRjmTiRdu3acerUKb6YNw/LRRed/lhVZdGVz/PZl1F8wtXsoD/jWMH8\nxy5jRG6EHay8AAAgAElEQVQuzJoFBkMLlr71k1AmRGPt/Bes+R34nNDpPMj8EowdoMdlTbrckiVa\nf97avjRKsaQE+pEN/sVg5v5iLgfXHuStc99i/L3jie4QTfqkdOlrJoQQQoiw83g8AOj1+roPVBQs\nXzyEZc4c/jh9FFfyOUvIwMIyrn57LrevvRfL2lelxqyKhs8YJ4SA/M9g1e1aIFN0MPT/tEDWEHY7\nFBQA8A43Mx47AKdOwZdf1n96Qq8Ebl56M+fcdg7eci9Ln1/KwscXMjtrNgX2gia+ICGEEEKI+tnt\ndhwOBwBTp07FbrfXPEhVYfNmeOUVmDoV312/5QBdmcIC9HhwYmY205m8/m/YH/+qhV9B6yahTIjG\n2PxMlQ0FCmv5g3Q6NhsVo51WDPRRYd26hl0iyhTFZbMuI2lgkrbDB16XlzxbXsPLIYQQQgjRSDab\nLbDucrkqt/fsgX//WxvEo2tXcodO5Y3f/cQ1/7ueLqd2MJyN/JGZeAMN9BTcRGHLkVqyqqT5ohAN\ndWI9nNzg39Brw9/XMqjHaVmtaAOCBA/0YTDAxRc3rig+jw/QRmzUG/WkW9MbdwEhhBBCiEawWq2B\ndaNej9Vuh169OLSnjIVMJpssFvIsefQMOq8HBWSRTSp7+St/wE0URtxYb5CuF1VJKBOioTY8qj2m\nXg0JI+oe1KMWG2MtdCCFNCoH+ujZE2bPBkvDL4PX7Q1MMp35eCa9L+otfcqEEEIIER5FRZCTgyU7\nO7DrCdcYPv5fFr/hOX5iSNDhHTnGJBaRRTZZZNM38QRK1mTIyuKSo8uw5eiwTkvCcuvQln4lrZqE\nMiEaYsdrcOAr0Jth9D/BlNyo0x0OuPJKyK6yLz4efvwRUhqZp47tOIbX5cWUYJJAJoQQQojQOnZM\nG4UsJwf7fw7ww94+xHOCg3QJHPJH/krFxNFmSslgcSCEjYjdhc6a6Z+s9Q4YMgR0Wo8pC2B5tOVf\nUlsgoUyI+hy1w+rfaus+F5za1ehQdvXV0CnXTiqVA30seyCdlJRGVJH5bfpwEwDOE05mZ81mevZ0\nCWZCCCGEaJqCAsjJgcWLYfFiSrbsxY6Fj/gl7/E8PnRo3S+WBE7RMYkZ3MCN7GC8YS3GCWNg8mTI\n+ieMGSPD3TeBhDIh6nPEBmh9uFBVbbsRzRbfegvmz4eHsaFQOdBHlt5GxbdMDXXqwClWvLIisF0x\nyIeEMiGEEELUS1Vh27ZAAGPxYgr3lrCEiSwmg8XczFpGVRmUI3AiqbxHvn9LoZy+mTvJfOxxmDAB\nzOaWfiVnHAllQtSn6mAe+sYN7rFmDdx1l7Zuw4qKgoKKYjD4B/5oOJ/Hx2fXfoa71I2iU0BBBvkQ\nQgghxOl5PLB+fWUIW7KE/KMmfwDLYDEPs4XBQafo8TCGlfRmF58zDS86jLh5aISDu9ZrxxhjYrDO\nnNm4TvGiThLKhKhP1VqxSd81uJbs+HG46iooL9e2l2OhwD/Qh/69txv9h2zh4wvJX5xPXNc4pr45\nlUMbD5FulYmjhRBCCOHncMDKlYEQpi5dxrbSHv4AdiWLeZG9pAedYsLBeJYHYpqF5cSN6AMZGdjL\nXdi2d8V6XTdGzXiLu0wfoCgK2dnZWCSQhZSEMiHq4/P6VxToNLFhp/jghhsgLy94f0LXGDgIjBzZ\nqCLsWLCDpTOXougVrvr4KtIy0+g7pW+jriGEEEKIM0xxsTYohz+EeVauZb17kD9e3cYS5nCUTkGn\ndOAkE1kSCGHnGDYRPXY4ZGZCxv1w7rnQoQPgH5jDf17FxNE6nU4CWRhIKBOiPqpbe1R02qAfDagp\ne/55+Prr4H2//S20/1L7g8a6dTBgQINuv/XzrXx+/ecATH52MmmZaQ0uuhBCCCHOUPPmsfgXf2eO\n6xdAOnuxsoxzKaFd0GFdOEgmOYEQNiQ2D/2E8f4QNhPGjgWTqd7bqaoaeLTb7RLMQkxCmRD1ObpU\ne1S9kD0ZshbWGcx8Pi2UVTVkCPztKjv8Uxt9kZtvhvT0epsw7vp2F59e9Smo2kTRqRNTm/FChBBC\nCHFGUFXsN/4Lq+tbfOiDnurNLjJYHAhivZOKUTIzICMDMm6C4cMhqvERYMUKbaAxn89HVlaWNGEM\nMQllQtTn2MrKdZ8TDiyoM5QVF8OJE8H7pkwBw1KbNuoRgNsNNludocxx3MG8m+fhH7ARFNibs5fU\nCRLMhBBCiLPatm0sOGEJBDIFH9fwCX/jAbqlGf21YBmQ8RD07w+K0uxbLllSOSS+y+XCZrNJKAsh\nCWVC1KeTFfQx4PU3PcyfCwN/D8b4Wg8vLg7eVhS44goAq7ahqtr8HXWMvug44WDOBXM4deAUKFot\nmYy0KIQQQggAsrMx4O9egQ8TTu65pYxuT66AlPAMAGat8rnFaDQGbYvmk1AmRH2SLTA5G/b/D/Lm\nwKkdYJsCk7+DqNgah586FbydmlpRIWbR/lDm58Pbpx99sSKQHVx7kITeCVz4tws5uuWojLQohBBC\nCM3ChexhKgCX8A2PT9uK5d8PhPWWEydWDnb2ww8/SC1ZiEkoE6Ihki3a0vd2+H4iFC6DH68A63zQ\nB3eOrR7KOlUd9CgmRns8zeiLzpNO3r/wfQ6uOUhCrwRmLJpBh5QODLiiYYOCCCGEEOIM5/WiLrLx\nHf8E4C88xNBH3gv7bRVFISoqCo/Hw5gxY8J+v7ONLtIFEKJNiU3Vas1MneFwNiy5BnzuoEOqh7J2\nwYMgnZbzpJM5F8zhwOoDWiCzaYFMCCGEECJg/Xo2n+zOQbrRlQMMid+vDd7RAqL8A4R4PJ4Wud/Z\nREKZEI3Vvi9M/h6MHWH/PPhmNBxZHHi6eihzV81sjipD4lex+/vdvDbkNQ6sPkB8z/hADZkQQggh\nRJCFC3mD3wAwhE0oTgdceSU8+yz88AMUFYXt1op/wJClS5eG7R5nKwllQjRF/FAY6R/3/uRG+OE8\n2P4PAJzO4EMXL4bZswG7HQqqDIlvtwPaiIofXPwBp/Zrg3pc+LcL6ZAqgUwIIYQQNdn/e4jXuROA\n77mIrs5crvnf9bz42DGWXvAkZfFdYeBAuPFGeP11WLOm2jfETbyv3R6YQHrq1KnY/Z9jRGhInzIh\nmsp5BO17DR+gwpp74PBCxg1+AUXpHRj9HrSJo6+400aHakPiq+PH883vvkH1afsVnULhtsKWfiVC\nCCGEaCNsJ4ZR+RFD5RBd+ZRr+JRrANDjYdi2jYzbtoJx761gHH+nf/RedOeMhHHjtMmix43T5ktt\nxFD5NpstsC5D4oeehDIhmqqTFfTR4HMBOtDpYd8X9D7wFd89fz9XPv4IJU6tQ1lxMfz2MyvvKQpK\nlSHxc57J4dC6QwAoehn2XgghhBB1s94znOg7XbhQMeLmDX5DOSZWMI6VjGUTQ1nHKNYxilncAUD7\n8iLGLFvFuGUrGMeHjONeOierwSFt7FiIr326HwgeEt9gMMiQ+CGmqFW/zm+DRo8era5evTrSxRBn\nq6N2OGLTAlpsGmz4I+yZDcBxR1d+9+5M3l96A6qqtRQ+ZEqjszMfPvyQTeoQPr/+c1Dg/Jnn4/P6\nZNh7IUJIUZQ1qqqObol7yXuREKIl2d/YhO0/x7D+LF6bYWflSlixAlasoPSnPNYwihWMCyz7qPnZ\nIpW9VY5YwSjWYu6fWhnSxo2DYcPAaAyc07FjR06cOMH8+fOZMmVKC77itquh70USyoQItcLlsOZe\nOLYSgBW7xnLP7L+zcvc4tjKAAWxn75wc5tzyI16Xl4tevojx946PcKGFOPNIKBNCnJWKi7V+ZP6Q\nxooVHDgIKxkbiGCrGEMJwcND6/EwlE1BQW2AcQ+6USMCIa3rvfdy6OhRDhw4QNeuXSP0AtsWCWVC\nRJLqgz3v41v3ELpyrXni7MW/4tyPl5Fw8gSvmX6Pz+lizN1juOTvlwRGMxJChI6EMiGE8Nu3Twto\n/ho176q1bCtLCapN28RQfOiDTmtHMWNYFTjqduZzCB/7J0+m23nnabVqY8dCx44RemGtn4QyIVoD\n9ylKVj6HYfeLREe5KCk0884jt3C8NJHYkX25f+W16KJkEFQhwkFCmRBCnIbHA1u2BDd73LyHNerI\noBq1AlKrndgD2M+lvM4kdgWaPcb27R7cP234cIiOjsQra3UklAnRiuxct5vVf3mSIwuTOHk0gcRu\nhewf0Y0rr1IZe9kkSJbRi4QINQllQgjRCCUlwc0eV67k4D5PUG3aIq4FjgKfA1cCWrPHIWwOHGWk\nnL1KTyb3KcByTSpkZoLFAnFxkXx1ESOhTIhWpMBewDsT3/EPfa9yzX0fM2D0dnwqqEoM+guyJZgJ\nEWISyoQQopkOHAiENPv33zNh7VpUwICeS3mAPH7JZobgrTGgu4oOH7fzOr/ifUbpNmAcPUwLaJmZ\nMHEiJCRE4hW1uIa+F0m7KSFawK6vd1XORYbKkVXJqCroFNCpDrwF8yJcQiGEEEKIarp1gyuvhJkz\nsV11VWB+NJ/iY9yYRawffiNFSgI5ZPBXfs8QNgEqoOBDz2vcjYXlxPuOkbXyOZ56wcyiqS9S1rGH\n1sTxt7+FuXPh0KEIvsjWQeYpE6IFlB0v86+p6PHSY89+XG4jRoMLRQHn5jeJTfs5JI6JaDmFEEII\nIWpjtVpRFAVVVTFER2N95RWwWIgtLSVj7VoyVqxgwkf/Imvt87gwosfLxXzNDvqzjYEsJIuFZAFg\nwMWYjavI2LiYzH++wwR+TYd+XSpr0jIzIS0twq+4ZUnzRSHCzOfx8ffef6cov4jhsTs5p/RHXkt/\nHltUOpeOWMCUEQsY1XM9XkzoJ7wHaVdHushCnBGk+aIQQoRWUlISx44d48svv2Tq1Km1HmN/YxO2\n9/dh7bMPi34l5ORweMdJljCRHDLJIZMNDEet0mBPh5fhbPA/m8NEltApNSY4pPXrB21wtGrpUyZE\nK7H1v1v59Oef0rFPR+7WvYqyYzuFi7cy7OoBHDwIBr2L1266k19Peks7YciTMPTJNvmHR4jWREKZ\nEEKEVufOnTly5AgHDx6kS5cuDT/x0CFYsgRyciAnh5Mb9rIMSyCkrWIMHgxBpwxgayCkZZJDSidX\ncEgbOhR0rb8nloQyIVqJ9ya9R54tj4tfuZhxr82A7dth61aWFA7AagWvF0Dldxe/zAvX/x69zgep\nV8P4dyEqJrKFF6INk1AmhBChlZycTGFhIUeOHCE5ObnpFzpxApYuDYS0slU/sdw3hsVkkEMmdiw4\nMAedks4eMlgcCGl9OxxFyZhYGdJGjQKD4TQ3jJyGvhdJnzIhwujwxsPk2fIwtjMy4sYR8FrlcxMn\nwvPPwwMPACi8/M19bD/Yn//cfy0x+Z9CSS5kfgnmbpEqvhBCCCFEgFf7Jhldc2uoEhLgssu0BTCX\nlDB5+XIm5+RAzp9w2dewxjWEHDJZTAZLmEgePcmjJ3OYDkDnokNkzs8hY/5iMrmVoTG70U2wVIa0\nsWMhpu18uS2hTIgwWvH3FQCMuGkE0e1rTqJ4332wbBn85z/a9tcbLmX0I3aW/+Vy2h1fDd+OgfPm\nQcdzWrLYQgghhBA1+Hw+APR6fWgvHBcH55+vLYCxvBzLqlVYcnJ4KOefeJdcz6bSnlUaM2ZymC7M\n5WrmovXFj3ecYOIPS8j8IYdM/sAow2YM40ZVhrRzz4V27UJb7hCS5otChElZYRkvpbyEx+nh7h13\nk9g3EQYMCDRfZMAAAIqLYfRo2Lmz8tzuSYVse/PnxJUtBn0MWGZD6lUReiVCtE3SfFEIIUKrXbt2\nlJSUUFRURPv27Vvuxh4PrF8faO6o5ixmx4mkoJCWT/BojWZKsWAPHDFOWUXMOYOC50pLTAx70aVP\nmRARtuCOBayetZoelh7csuwWbWdaGuTnw4cfwi9/GTh20yYYNw4cjsrzOye7WDPrdro739F29Po1\nxPWEzpNkomkhGkBCmRBChJbJZKK8vJzs7GwmT54cuYL4fLBlSyCk8eOP7D1kDPRJyyGT7QwIOsWA\ni7GsDPRLO5dlbOl6Pra4y7A+cA6W24aFpagRD2WKovwVuBxwAbuBm1RVPel/7o/ALYAXuEdV1W/9\n+y8GXgH0wL9VVZ1Z333kjVC0RvmL83nnvHdABX20nhmLZpDCPpgwAVQVTCZYuBAsleFq9myYMSP4\nOlFRKjv/9yLpx3/v36OA3gSTsyWYCVEPCWVCCBE6drudc889F4CYmBiys7OxWFrJZxFVhd27K0Na\nTg6H95SymIxAUKs+DL+C1/+oEo2L7H/txnLr0JAXraHvReEcR/J7YIiqqsOAHcAf/QUbBFwLDAYu\nBl5TFEWvKIoeeBW4BBgE/NJ/rBBtztb/bqVi2nufx0eeLQ9sNu2PBoDbrW1XMX06/OY3wdfxeBQ+\nWvcA9Jzu36OC1wVHgs8VQgghhAgnW5XPLS6XK2g74hQF+vSBm2+Gd9+F3Fw656/mqg9+ziu3bWXd\nwOs5TkfmM4U/8BfGY0cBVPT4iMKFAdsnhyP6EsIWylRV/U5VVY9/cznQw79+BfCxqqrlqqruAXYB\nY/3LLlVVc1VVdQEf+48Vos1J7FfZRllv1JNuTQertXLuMYNB267m/vuDtxXFf1if2wn8d9VFQaea\n5wohhBBChIu1yucWo9EYtN0qpaTAddfBrFmwZQvxh3cw5T+38Jd7D2IfeRffcQEK/oFL8GE1LY9o\ncVtqxrWbga/9692BgirP7fPvO91+IdqcuK5xACQNSGJ69nRSLClaU8WUFO2At98OarpYoWvX4G2D\nAcaPR2uq2F0bNpa+d0jTRSGEEEK0KIvFgsE/D9hXX33VepouNlSnTvDzn8PLL8PatWQ9PYnHeRqA\nBI4zatHf4HDkasuaFcoURflBUZTNtSxXVDnmUcADfFCxq5ZLqXXsr+2+tyqKslpRlNVHjx5tzksQ\nIizKCssA6D6uuxbIKlTMlzFyZK3ntW8P5ipzJbpccPKkf6NLlvboKQlxaYUQTSHvRUKIs03F/GTj\nxo2LcElC4L77eCJpFkPYxGG6MssxHZ55JmLFaVYoU1X1fFVVh9SyfAmgKMoM4DLgerVyRJF9QJVP\nqfQADtSxv7b7vqGq6mhVVUc3azZxIcLEcUwbRjEmsXGTFipKzdqygwf9K/H+zqcnNzezdEKIUJD3\nIiHE2UZRaqtDaaPi4tA/8SjP8QgAz/Iop2Z9ALm5ESlO2Jov+kdSfAiYqqpqWZWn5gHXKooSrShK\nT6AvsBJYBfRVFKWnoihGtMFA5oWrfEKEU9kx7VfenGiu58iaThvKOgzRHos2g+prRumEEEIIIZqu\nrU+pFXDrrVyWthkLyzhKJ17y3A1PPhmRooSzT9k/gXbA94qirFcUZRaAqqo/AZ8CW4BvgLtUVfX6\nBwW5G/gW2Ap86j9WiDanqTVlUEcoMyWDqbPWfLE0v5klFEIIIYRonIqasjMmlEVHo/zpaWbyMAAv\n8HsK3/8GNm5s8aKEc/TFPqqqpqiqOsK/3F7luWdVVe2tqmp/VVW/rrL/K1VV+/mfezZcZRMi3CpC\nWUhryqCytuzkpiaWTAghhBCiac6o5osVrruOzKEnuYSvOEV7/szD8MgjLV6Mlhp9UYizSqD5YlLz\nQ9mBqj0rK/qVFUm/MiGEEEJExhlTUwag18Nzz/EsjwLwKndRsGADLF7cosWQUCZEGISl+SJAvNSU\nCSGEECIyzrjmixWmTGHkhFiu5SPKMfEUT8If/wgt+DollAkRBhVD4jel+WK3bsHbwc0XpaZMCCGE\nEJFxRjZfBG3465kzeZon0OPhHW5i29JCWLCgxYogoUyIEFNVNdB88djOY8FPOrQaNNatO+351WvK\ngkZm7TBIeyz6CY60bLW6EEIIIc5uXq8XgBUrVkS4JGEwcSJ9p/Tn1/wbH3p+wVzst70L/tccbhLK\nhAix3B9yUb1adfcHl3xAgb1Ae8JuhwL/+s03a9u1qB7K9u2rcujJTYCiDYm/8EI4Wvs1hBBCCCFC\nyW634/B/uTx16lTsp/kc02YdPAhmM+dhA2AzQ5l0YA72Bz5rkdtLKBMixPba9gbWvS4vebY8bcNm\nq2yb7HZr27XYXEvLxMChh34A/NfwueFI7dcQQgghhAglW5XPLS6XK2i7TSsshAcfpLTnEJ6fm84t\nvBV4yo0B28KWmRtWQpkQIZaelR5Y1xv1pFv921ar1mYZwGDQtmvx/vvB23p9lUPVKlXoeiN0qv0a\nQgghhBChZK3yucVoNAZtt0knT8ITT1Ce3p9/vOCkd/lPPMTzOIhFhxcdHqJxYf11nxYpTlSL3EWI\ns0hPa8/A+q+++xUplhRtw2KBlBTIz4e339a2q3E6Ye7c4H1/+UuVQw9na489fgYD/wDJNa8hhBBC\nCBFqFosFk8mE0+lk3rx5WGr5HNMmlJTA3/+O+/mXeK/oCp5mHQWkAjCaVTzDY7RLMPBjt19ivWsw\nljvGtEixJJQJEWKKTsEQa8Bd6qbz8M7BT8b4h8gfObLWcxcsgKKiyu3kZLjnHv/GyZ/g6BKIigPL\nbDC0C33hhRBCCCFOQ6fTGtmNHz8+wiVpAocDZs3C+9xf+Lgwi/9jGbvoC8BQNvInHmdq19Uojz8G\nt9zCuUZjixZPmi8KEQbGWO0/srvU3ajzqjddvPZaraUjALv+pT2m3yCBTAghhBAtrmJ+sjY1NL7L\nBa+/jtq7D5/fv5jhhT9wAx+wi770ZQcfcS3rE8/nir+dh7J7F9xxB7RwIAMJZUKEhTFO+8/sKnE1\n+Jzjx2tOh3HDDf4VTxnsma2t970tBCUUQgghhGicNhXKPB54913Ufv35+s55jD44j2l8zk8MIY08\n3uYmtnQ4l2ufHYYuLxfuv7+yRVMESPNFIcLAEKtVb7lKGx7K5s7VBmWs0LcvjKloxrz3E3AXQeI4\nSBgRwpIKIYQQQjROqw5lPh98+ik8+SS2HV15jDksZSIAXTnAYzzDLbGfEH3fnfDALoiPj3CBNRLK\nhAiDiuaLjakpq9508YYbKgdrDDRd7CO1ZEIIIYSIjIqaslZJVWHePHj8cZZvMvMYr5LN+QAkUsgf\n+TN3RL+D+be3wB+2aR33WxEJZUKEQUXzxYb2KcvLgyVLgvddf71/5fg6OLYCDB0g7ZrQFVIIIYQQ\nohFaZfNFVYXvvoPHHmP9ajeP8yzzuRyADpzk97zAvVGv0e626+CRzdCtW4QLXDsJZUKEQaD5YgNr\nyj78MHjbYoHevf0bFbVkPWdAlDlEJRRCCCGEaJxWF8pycuCxx9i6+ChP8hRzuRqAWEq4l1d4QPcy\nHW+6Ah5fB2lpES5s3SSUCREGgYE+GtCnTFVhzpzgfYEBPtynIO8DbV0G+BBCCCFEBLWaULZyJTz+\nOLnf7eQpnuR9bsCHnmic3MlrPMxf6HTd+fDkUujXL7JlbSAJZUKEQWNqytatg23bKrejouDqq/0b\neR+CpwSSM6DDoDCUVAghhBCiYSIeyjZsgCeeYN+8NTzDY7zFLXgwEIWbW3mDx3iG7leOg6ezYciQ\nyJSxiSSUCREGjZmnrPoAH5dcAklJaFVoMsCHEEIIIVqJiIWybdvg//6Pw58sYiYP8zqfUI4JHV5m\n8C5P8DS9LhkAf5oH55zTsmULEQllQoRBQ5svejzw0UfB+wIDfBxbBSfWQXQipE4LQymFEEIIIRqu\nxUNZbi48/TTHZ8/nBfV+XuEtyogF4Go+4SmeZIC1KzwzByZMaJkyhYmEMiHCoKL54p7sPfS+sDcp\nlhTtCYdDe1y3DgYMYOFCOHSo8rx27eDyy/0bm/+kPXa5EPSmlim4EEIIIcRpVISy5cuXMyGcIWjP\nHuwzZvHNYjMHOJdPeYViOgBwOfP4E48zfFwMPPsqTJ5cZQ6htktCmRBhUHqkFID8JfnMzprN9Ozp\npLAPCgq0A26+GdLTefFFS9B5GRlgNgMHv4MD87WdBf+Fo3ZIDj5WCCGEEKKl2O32wPoFF1xAdnY2\nFkuIP5ts2QIzZ/L9nENMYT5uDIAWuM7ne57hMcYNL4dnnoUpU86IMFZBF+kCCHEmKsov0lZU8Lq8\n5NnywGbT+okBuN04vraRnR18Xpcu/pVtr1TuVN1wxBbeAgshhBBC1MFmswXWXS5X0HazrVkD06ZR\nOPg8HpvTj8v5EjdGtEDm49e8wfcD72Xc3Adh7Vq47LIzKpCB1JQJERZdR3Vl62dbAdAb9aRb04Eo\n7Q+IqoLBwNcOKx5P5TmKolWg4fPAibX+vTrQGaGTtWVfgBBCCCFEFeedd15g3Wg0YrVam3/RxYvh\n2Wc58O1GXuD3/IvZgT5jOryASjQubr5JgTc3gV7f/Hu2UhLKhAiDbqO12eITeiVw5ftX+vuUpUBK\nCuTnw9tv89zfgqv8b7rJ30c1/0twHoKY7tD3Tug8SZouCiGEECKiqjZVbFbTRVWFb7+F555jz+IC\n/sJDvMOXuIgG4FIW8AjPoevSGVvSNKx3DsZyx29C8RJaNQllQoRBdDvtD0tMYkzlIB8AMTEAbIsZ\nyZo1lbsVBZ54wr+x/SXtcdD/s3fn4VHV5//G7zPZWUVkkUUQQQUVRRGIS03dd2u1alsVd+tSRaV1\n4dtWRbEoilD3Ba3a+qtVcUVcaKO1BhCLokIVVASRqqggIFnn/P6YSUjYJDAzhwn367pSZs6cOfNA\nTTLveZ7zOVfADhdmoFpJkqR1q13kA9iwQBaPw1NPwYgRzHzre/7IFfyVX1BDLgFxjufvXMUI+vUL\nYNgwOPZYimObz5lWhjIpDfJbJpfEX7rmJfEff7zh/QMPhG7dSCyD/9W/Ia819DgtvUVKkiStp/qh\nrFFqr/9zww38Z1Yh1zOM8RxLSIwcqhnMg1zOSHrv3RaGjYBDD21y54utD0OZlAYFrRKdsoqlFWt8\n/NlnG94/66zkjf/emviz59mQ1yJN1UmSJG2Y9b5GWXk5/PnPMHIkr3/Sieu5mYkcBkAB5ZzBOH7L\njdVATngAACAASURBVHQ/ZEe46m740Y/SWPWmz1AmpUHt+GLFd2sOZUu+W3m7bVs45hjg+wUw7zEI\nYrC9Y4uSJGnTsd6dsuXL4e67CW8axcv/25nreYDXSCwS0pxl/Iq7uIyb2frYYrjqcejfP41VZw9D\nmZQG+S2S44vLKgnDcJ2fKp18MhQUAO/cAWE1bPMzaN4tQ5VKkiT9sB8MZd9+C7fdRvzWsTz9zT6M\n4GmmsScAW/AtFzGWi2K30/YXh8AVr8BOO2Wg6uxhKJPSIJYbI7col+oV1VQtr6oLaVVVkLfKvmee\nCVR/D7PvSmzYYUhGa5UkSVpfq33Q/MUXMHo01bffzd+WHc4N/JP32RmA9nzBpdzCeXn30+r04+Dy\nydCjRwRVb/oMZVKaFLQsoHpFNRVLK+pC2eIl0K7ePnvuCbvsAsx5BCq/gbYDYCuXv5ckSZuW1Tpl\n8+fDTTdRcc+feajiBP7IND5mOwC6Mo/fcBNnFj1Ks1+dCpe9A507R1B19jCUSWlS0KqA5V8uT6zA\nuDXU1MCSVULZmWeSuF5H7QIfOwzZLFcckiRJm7baUBYAnHkmyx96gnurT2MU77OALgD04kOu4I+c\n3OpZ8i/6FVz8X9hqq+iKziKGMilNapfFr12B8ZVXoFv1yseLiuCkk4CFL8F3sxIXi97m+AgqlSRJ\n+gHvvgtATjzOiHEdGM1sFiU/at6FGVzFCH62VSk5l14M58+B1q2jrDbrbD5XZJMybNUVGO+/v+Hj\nP/tZ8ufVB8ku2fYXQmzVM84kSZIiNHkyHH00X+15CACV5DOMESyiHQOYwtMczdudj+SkMXuR8+nH\ncOWVBrINYCiT0qT+BaQXLUpcxL6+s84ClsyChRMhpwh6npP5IiVJklYVhvCPf8ABB7Cg+DguefbH\nbM8HyQcDfsw/eIUDmLzdyRx93zHEPp4DF10EzZpFWnY2c3xRSpP6F5B++OHEyou1tu0O++wDvDkm\nuWEwFGyZ6RIlSZJWCkN47jkYMYKPJ3/BSC7nQU6jkgJgBQD5VPKPnS+Gq65KjP3kGidSwX9FKU3q\nzin7rnK10cWf/hSCyq/hk4cSG3a4OMPVSZIkJdXUwOOPw4gRzJxRxQ1cyaP8nBpyCYhzAn9jCNez\nFxDmxOCddyDmwF0q+a8ppUntOWUfzazg/fcbPvaTnwBz7oGaFbD1YdB6x8wXKEmSNm+VlTBuHPTu\nzVsn3chxM37PTszkEU4hIOQ0HmAWvfnbj++m77MjAAiDwECWBnbKpDSp7ZS9+e/K1R5r17YSPrgt\ncWdHLxYtSZIyaMUKuO8+uOkm/jW/G9czlhc5FIACyjmL+/gNN9HtyL5w1YNQXAzLlwNruHi0UsJQ\nJqVJbads6Tsf04WefEZXCpPz2Lx9LfA5NO8OHQ+KrEZJkrSZWbgQDjyQW2ceyBheZS7bAtCCpZzH\nnVzKaDqe8CO46hnYdde6p9Vep6ympoaysjKKi4sjKb+psvcopcnyrxKfKHWKf8ZgHuJQXmAb5kNP\nIP5EYqcVC2DR5OiKlCRJm494HE47jaEzT+MSxiQDWcgZ3MenOdtx4+n/peMHr8Lf/tYgkAFMmTIl\neYg4BxxwAGVlZRH8BZouQ5mUJovnLgYgAGLUsAdvERBCn+RGSPxw/LI0ogolSdJm5fbbmfnSfMaw\n8tSJHKrp2bWKLT96M3F+2fbbr/Gp//rXv+puV1ZWUlpamu5qNyuGMilNOu3RCYAQiJPDW+xBSABf\nsDKU5eRD+5KIKpQkSZuNWbOo/M0wfslfqCaPHKrJoYp8qij5v32gW7d1Pn3fffetu52fn09JSUma\nC968eE6ZlCadB3QGYDGteYLj+IyuLG3dldbhvMQOLbeHQQ9CO2eyJUlSGlVWwskn84eKK3mbfvTg\nI+7Ov4g3B15IycldKD5nlx88xKBBgwCIxWJMmjTJc8pSzFAmpUl+i8Tqi+UU8RldASjYoojkTej6\nUwOZJElKv2uu4bX/NGcklxOjhkc4meJ7z+PAUw9r9KFisZiBLA0MZVKa5DZPhLJ8Vi6Jn5fPylC2\nRd/MFyVJkjYv//43S264g1OZTkiMYQyn+PgucMopjTqMS+Gnl6FMSpPFyxOhrIAKALbYAnJiwDbJ\nHbb44VEBSZKkDfbdd3DKKfw6HMOndKc/b/L7jvfCXdOhkSHLUJZehjIpTRYsSlynrLZTtu22QHUc\nOgBBHrTaIbriJElS0zdkCI990p+HOZUivucRTibvz/dB27YbfMja65UptQxlUpp8+nkeAPlUERDS\no0cAixNdMwq3g1hehNVJkqQm7ckn+eyBl/gVMwC4hUvZ4deHwMEHb9Dh7JSll6FMSpO5nwZUkE8B\nleRRybbbFsCCZCgrWvM1QCRJkjbawoXEzz6X03iUb9mSI3iOc3d8DUa+FXVlWguvUyalySefQCW1\n55VV0qMH0L42lDm6KEmS0iAM4cwzGfvNL5nEgWzFV9yX8yuCvzwCRUUbfNjaTpnji+lhp0xKk48/\nhlasXIFx221ZOb5op0ySJKXDXXfx3gvzuIInAbiPs+g4/ALYffeNOqzji+llp0xKk/qdsnwq2LZ7\nCB0MZZIkKU0++ICKS6/kl/yFCgo5i3s5Zu+v4be/jboy/QA7ZVIaVFTAggVQQWIFxgIq6dZ+PhTG\n4Tsgt120BUqSpKalqgpOPpn/Kx/GDHZlO+Ywuvnv4OEyyMnZ6MPbKUsvQ5mUBp9+mhjpru2Ubb1l\nJYUrZicenEejrw0iSZK0TsOH889pLbiZy8ihmkc4mRZ/uiF5TZ7U8Zyy9DCUSWnwySeJP2tDWad2\nlbA4sSQt8yMqSpIkNU1lZSy+7jYG8zYhMYYxnEHHdoLTTkvZS9gpSy9DmZQGq4ayjm0qDGWSJCn1\nli2DU07hgvBPzGcbBjCF/+twH9wzPaWTOYay9HKhDykNPv448WdFMpRt1apep2xeREVJkqSm55JL\nePSjPfkrv6QZy3mEk8l74B7Yaqu0vJzji+lhp0xKg1U7ZVs2/x6++wDiwILo6pIkSU3IM88w774X\nOY/EB7+3cCm9zj8YDjss5S9lpyy9DGVSGtR2yiqTqy9umfs/CGvgm3yorIywMkmS1CR88QXxM8/m\nNB5lCVtwJM9yzvavwk3/iboybQBDmZQGq3bKiuJfJDZ8mQ8YyiRJ0kYIQzjrLEYvOpl/sj/t+JL7\ncn5F8JenoVmztLyknbL0MpRJKbZ4MXz7beJ27TllwfffJDZ8UQAsi6YwSZLUNNx7LzOe+5SreByA\n+zmTDlefB/37p+0lDWXp5UIfUorVdslgZads4fvlzJ/dBVZUJx6YPj2CyiRJUtabPZvSCx7jYF6i\nkgLO4W6OKv4arrgiYyW88cYbGXutzYWhTEqx+qGsFd8BsGheCx4aMZj53VtCT+CMM6CsLJoCJUlS\n1ir75W0cVP0CX9CRgDgn5I2Hhx+G3PQOwJXVe99y4IEHNrivjWcok1JsWb3pxK1YlLwVUFMdY+6H\n3aEPUFUFpaWZL06SJGW10tmdqSYneS9kaoejYbvt0v+69d63VFZWNrivjWcok1Ks/mVB5rFN8lZI\nTm6c7r3mwkwgLw9KSjJfnCRJymolB+SQT1XyXsC+3z4N5eXpf91671vy8/Mb3NfGM5RJKdau3crb\nn9INgPzCSk696s90LauAOcC4cVBcHE2BkiQpaxXffxav5B9BG74hJMZXy5slxhfT/br13re89NJL\nDe5r4xnKpBRr337l7YrkdcpCYnTt9RksbpV4oF+/CCqTJElZr3Vr9r2oH9fwBwBGMRRuvhni8bS/\ndCyWiA4GstQzlEkpVr9TVkU+IVBVnkc8HkBuGFldkiSpibjoIk7PeZg2fMMb7M0bH2wJzz0XdVXa\nCIYyKcWaNYPmzRO3Q4K6blnligJDmSRJ2nhdu9Li50dxHncCcDOXwahRGXv5MPT9TKoZyqQ0qN8t\nqw1lFYYySZKUKkOHciG3kU8F4zmWOf/6HKZMSetLegHp9DGUSWmwpvPKKlYUQI6hTJIkpcCuu7L1\nQbtwMo8QEmM0l2S0W6bUMpRJabDWUJab/pNwJUnSZmLoUC7lFgAe4HQWPfEqfPRR2l/W8cXUM5RJ\nabCm8cXy7x1flCRJKXTQQezUN5fDeZ4VNOPO8Fy49da0vZzji+ljKJPSYO2dMkOZJElKkSCAoUMZ\nSmJs8U/8mvL7/wJffx1xYWosQ5mUBmtd6MNzyiRJUiqdeCIlnWazO2/xFe15eMVxcOedaX1JxxdT\nz1AmpYGdMkmSlBH5+QRDLuYybgYSy+PHx94G5eUpfynHF9PHUCalgZ0ySZKUMeecw89aTKQr8/iA\nHXn+qz3h4YfT9nJ2ylLPUCalgasvSpKkjGndmrxzz2AIiUU+RjEUbr4Z4ql932GnLH0MZVIaePFo\nSZKUURdfzFk5D9KKJbzGfkz9oBU8/3zUVWk9GcqkNFhTKKs0lEmSpHTp2pVWPz+Cc7kbSJxbxk03\npeWlHF9MPUOZlAaFhdCqVeK255RJkqSMuOwyLmIsuVTxOMcz91/zYMqUlB3e8cX0MZRJaVLbLVt5\n8ehCO2WSJCl9dtuNLgf25uc8SpwcbmVI4twybfIMZVKa1C72Ue45ZZIkKVN+85u65fHv4yy+fXwS\nfPxxSl/C8cXUS3soC4JgaBAEYRAEWyXvB0EQjA2CYE4QBDOCINi93r6DgyCYnfwanO7apHRatVPm\n+KIkSUq7gw5i111CDuIlltOCu8OzYfTolBza8cX0SWsoC4KgK3AQMK/e5sOAXsmvc4A7k/tuCfwB\nGAgMAP4QBEGbdNYnpVNtp8wl8SVJUsYEAQwdylBGATCWi6i4/xH4+uuIC9O6pLtTNhr4LVC/PXAM\n8FCYMBnYIgiCrYFDgJfDMPwmDMNvgZeBQ9Ncn5Q2tZ2ySvITf5YXENopkyRJ6XbSSRzUaSa7MIOF\ndOLRFcfAXXel7PCOL6Ze2kJZEARHAwvCMHxnlYc6A/Pr3f8suW1t29d07HOCIJgWBMG0r776KoVV\nS6lT2ykLiVETS3yrVcbzIqxIUir5u0jSJis/n2DIxXXdslEMJRz7Jygv36jDOr6YPhsVyoIgeCUI\ngvfW8HUMMAz4/ZqetoZt4Tq2r74xDO8Jw7B/GIb929W/IJS0CakNZQA1OTkAVFQbyqSmwt9FkjZp\n55zDSS2epxMLeJ+defHL3eCRR1JyaDtlqbdRoSwMwwPDMNx51S/gY2Bb4J0gCOYCXYD/BEHQkUQH\nrGu9w3QBPl/Hdikr1X+PVh3LBaCiOj+iaiRJ0maldWvyzz2dixkDJLpl3HwzxDf8/HY7ZemTlvHF\nMAzfDcOwfRiG3cMw7E4icO0ehuH/gGeAU5OrMA4CloRhuBB4ETg4CII2yQU+Dk5uk7JS/U5ZPEz8\nEJv3VXvoCUyfHk1RkiRp83HxxZyTM44WLGUSB/Lr/55P2YV/2eDD1dTUADAlhRekVkIU1ymbQKKT\nNge4FzgfIAzDb4DhwJvJr2uT26SsVBvKujCf5pXfA/DC44cx/4QucP1pUFYWXXGSJKnp69qVLU46\nlCN4DoDbuIAD7jyOsjvfbvShysrKqKioAODII4+kzPcxKZWRUJbsmC1K3g7DMLwgDMPtwjDcJQzD\nafX2GxeGYc/k1wOZqE1Kl3btEqvSdmcuQfL0yHhNjLmzu0PPKigtjbQ+SZK0Gbj8cnrwMYmlGmJU\nkE/pmFXX4fthpfXet1RWVja4r40XRadM2izk5kLbtjCX7oTJdWxiOXG695oLc/KgpCTS+iRJ0mZg\nl104au/F5FEFJFaF3ufTv8CiRY06TEm99y35+fkN7mvjGcqkNOrRAz6jKx/SE4BBh5bR9e+fwbAH\nobg42uIkSdJmofiJobycfyRb8jUhMaaX7wi/X9Mi6es4RnEx+fmJBcsmTJhAse9jUspQJqVRly6J\nPxezJQAt2iyDBUC/ftEVJUmSNi8dOrDfdQfxAKcD8AeuYdFdj8O77zbqMLWrLw4cODDlJW7uDGVS\nGnVOXv68isT1yaoq8qB5hAVJkqTN00UXcVSPmRzESyymDb8Pr4YhQ6AR1xyrvT6ZS+OnnqFMSqPa\nTlllXSjLN5RJkqTMKyggGH0Lo7mEHKq5m3OZ8Y+v4Omn1/sQhrL0MZRJabRap6zSTpkkSYrIUUex\n04GdOJ87iJPDEG4lvGwoJJe6/yGGsvQxlElpVNspc3xRkiRFLghg9GiuDq6lLYv4J/sz/uO+MGbM\nej3dUJY+hjIpjeyUSZKkTcrOO7PleScynN8BcBk3Uz78Jvjf/9b7EIay1DOUSWnkQh+SJGmTc801\nnN367+zCDOayLbcsOxuGDfvBp4WNWBREjWMok9KoqAi23NJOmSRJ2oRstRW51/6eWxkCwAiuYsG4\nF+E//1nn0xxfTB9DmZRmXbpAFYmLLdopkyRJm4TzzmP/3v/jpzzBclpwJSPg4ovXuUS+oSx9DGVS\nmnXubKdMkiRtYvLy4JZbuInfUEA5D3Mqk1+vgr///QefaihLPUOZlGaJTpnnlEmSpE3MoYfS4/De\nXMbNAFzMGOJDfwsrVqy2a/3zyQxlqWcok9JstU5Zi4gLkiRJqnXLLVyZcxNb8zlTGcgj838Eo0ZF\nXdVmx1AmpZmdMkmStMnaYQdaXHQGI7kcgCv4I0tvuA0WLGiwmysvppehTEqzzp2h0nPKJEnSpur3\nv+eXbV9kIJNZSCdGrBgCV1zRYBdDWXoZyqQ069IFqpOhrLIin7AQCOPRFiVJklRriy2IXXctY7gY\ngFu4lI8eeQMmT67bxZUX08tQJqVZ584QJ0Y1ORAG1NTkElYvjbosSZKklc4+m4F9yzmVP1NJAUMZ\nlVgiP574INlOWXoZyqQ022ILaNas4WIfS5d8F3FVkiRJ9eTkwK23cgNX0pxlPMWxvDK1JfzlL4Cd\nsnQzlElpFgSrrMBYkce3Xy2JuCpJkqRV/PjHdDp2EMO4HoAh3Er15cNg2bKIC2v6DGVSBjRYgbEy\nj+++MZRJkqRN0KhRXJJ3O9vyMe+zM3cvPApGjnR8Mc0MZVIGrNopW+b4oiRJ2hT16EHhZRdwM5cB\n8DuG8/WN9xPOnQs4vpguhjIpA+p3yior8ihfbiiTJEmbqKuu4icdJrM/k/iWLbm68krCq66Kuqom\nzVAmZUCDTlllHlXlhjJJkrSJatmS4IYR3MoQYtRwJ+fx3pOzADtl6WIokzKgwTllFXmEVYsjrkiS\nJGkdBg9mlz0K+BV3UUMuv2Vk1BU1aYYyKQNW7ZRRY6dMkiRtwmIxuPVWruX3tOEbStkfgBwX/EgL\nQ5mUAV26QGW9UJYXGMokSdImbp99aHvSwVzL74FEGMupqYHvfB+TaoYyKQPat4fqYOX4YlH+d6xY\nEXFRkiRJP2TkSH5V+Gd25H0AqsmD666LuKimx1AmZUBODhS2SISy2dO3p1XFEr7+b1nEVUmSJP2A\nbbYh97eX8kcuB6CSgFNvWkrZ9ZMiLqxpMZRJGbJN+CkAH727HU/d9lOqnzoZvjKYSZKkTdxvf0vz\ntu8l71TyMH+m5P9ilN3zbqRlNSWGMilD2lZ8nrwVUFMdY96szvBlaZQlSZIk/bDmzfnXXnvV21BJ\nFf+m9IlFkZXU1BjKpAxZvkXn5K04OblxttlxAbQvibIkSZKk9bLvmWfWu5dPLvtQMsAT5FPFUCZl\nSHWv3gB07TWfU6/6M9M6PgjtiqMtSpIkaT0M3D+xJH6MHGASP+MzivE0jFQxlEkZUtgs8e3WaZuF\ndO31GR99s0vEFUmSJK2fIAgAKCAEiplMMeHfH4+2qCbEUCZlSFGzxA+zeGXi265y2dIoy5EkSWq0\ngDjt+YKP2Y53PiiAmTOjLqlJMJRJGVLUPPHtFq9KhLPK75dFWY4kSdJ6q+2UhUHAsYwH4El+Ck88\nEWVZTYahTMqQZslQFlYnO2UrDGWSJCm71AQBP+VJAJ7gOHjcEcZUMJRJGVLUouH4Yk25oUySJGWH\n2k5ZPBbjx8GrtOEbZrIT/51RAXPmRFxd9jOUSRnSvEVtp6w2nBnKJElSdqgNZQB5JXtzNM8AyW6Z\nI4wbzVAmZUhtKIsnxxepdqEPSZKUXcIwhOOOqxthfJKfOsKYAoYyKUOat0x2yJKhLFZjp0ySJGWH\n+p0yjj2Wg3mZFizlP+zBJ9MWwaefRldcE2AokzKkecuG44uGMkmSlC0ahLJOnSjcew+O4Hkg2S17\n8smIKmsaDGVShhQWNeyUFeYto7w8yookSZIaJwzDxA1HGFPKUCZlSCwveU5ZTeLPFoXLWLIkyook\nSZLWT4NOGcBxx3E4EyhkBW+wN5+/8Ql8/nk0xTUBhjIpQ2I5DccXWxQsY/HiKCuSJElaP6uFsm22\nocWefTiEFwEYz7EwfnwElTUNhjIpQ4Kc5PhislPWsmipnTJJkpRV6sYXwRHGFDKUSRkSy11lfLHA\n8UVJkpQdVuuUARx3HEfxLLlU8Sr7sejV9+GrrzJfXBNgKJMypG58MZ4cXyx0fFGSJGWxnj1ps2s3\nDmASNeTydHgUPPVU1FVlJUOZlCGrji+60IckScoWtZ2yBuOL0GCE8QmOc4RxAxnKpAypHV+0UyZJ\nkrLNGscXAY4/np/wFDFqeIUDWTJpGnz7bWaLawIMZVKG1I4vxuOeUyZJkpqI3r1p33sr9uVfVJHP\nczWHwjPPRF1V1jGUSRlSN74Yd/VFSZKUXdbaKQNHGFPAUCZlyGrji16nTJIkZYl1hrLjj68LZRM5\nlOUvvg7ffZehypoGQ5mUIXXji8SoiccozK9g2XdVEVclSZLUOKst9tG3L122K2Qgk1lBMyZW7Q/P\nPx9NcVnKUCZlSN34IjG+L28GQOX3y6MsSZIkqdFWC2VBAMcfz3E8ATjCuCEMZVKG1I0vErC8vDkA\nld8vi7IkSZKk9ba+55U9x5FUTJgEy/3weX0ZyqQMqT++WNspq6kwlEmSpOyyWqcMoH9/ttumml15\nm6W04pXyvWHixMwXl6UMZVKGrGl8kaqlEVYkSZK0/tbZKQsCOO44Rxg3kKFMypD644t1oax6GWv6\nsEmSJGlTtcZOGTQYYXyaY6h6diKUl2ewsuxlKJMypMH4YkUilDXLX8YyJxglSVIWqO2UrTWUFRfT\np+O37MB/+Ya2vLZ8d3j55QxWmL0MZVKG1B9fXJ7slLUo9FplkiQpO6xzfBEgFiM47qeOMG4AQ5mU\nIbWdsvqrL7YoXMaSJVFWJUmS1Dhr7ZRBgxHG8RxL/OlnobIyQ5VlL0OZlCG155RVkgfzE580/WSP\np4h/WRZlWZIkSeulNoyVla3jvcu++7J723l05xP+x9acv2QEZb95MkMVZi9DmZQhnz/0CgA15DL7\nle2ZP7sLh+76Ar0/PwC+MphJkqRNV1lZGTU1NQAcdthhaw9mubkEPz2WQSQev4dzOGDs0ZTd826m\nSs1KhjIpQ+Y/Mz15K6AmHmPuzO7kxEJiVMKXpVGWJkmStE6lpaV1tysrKxvcX81PfkJzEheOTrzT\nyaP0ia/TW2CWM5RJGbLNT3ZP3grJicXp3mcuNfGAOPnQviTK0iRJktappKSk7nZ+fn6D+6vp2JG9\n+XfyTkg+VZQc1zad5WU9Q5mUIV3OOBiAGHE6DlpA116f8foH+1CaMwnaFUdcnSRJ0toVFxeTl5cH\nwIQJEyguXsd7l1iM3XgHgI4sZFLnwRSfs0smysxahjIpw2LE+aZN4tOiKR8N4uvAQCZJkjZ9sVgi\nOgwaNGjdOwYBAYlFQdrzFcVtP0x3aVnPUCZFbF2rykqSJGWdICBGHEhcn5V4POKCNn2GMilCtZ8i\nSZIkNRmxWN17nJDAT6DXg6FMikAYBlGXIEmSlB71xhcNZevHUCZlSLiGH0hBEPpzSpIkNS2xmOOL\njWQokzLMkUVJktSk2SlrNEOZFAXHFyVJUlPlQh+NZiiTIhQEfnIkSZKaGBf6aDRDmRSBkJWdMn9O\nSZKkJsXxxUYzlEmSJElKHRf6aDRDmRQhF/2QJElNjp2yRjOUSRHwOmWSJKnJqhfK7JStH0OZlClr\n+JDI65RJkqQmp974op2y9WMokyLgzyZJktRkOb7YaIYySZIkSanjQh+NZiiTIuRCH5IkqcmxU9Zo\naQ1lQRD8OgiCD4IgeD8Ighvrbb8yCII5yccOqbf90OS2OUEQXJHO2qQoeZ0ySZLUZLnQR6PlpuvA\nQRD8GDgG6BuGYUUQBO2T2/sAJwE7AZ2AV4Ig2D75tNuBg4DPgDeDIHgmDMOZ6apRiloQmMgkSVIT\n40IfjZa2UAacB/wxDMMKgDAMv0xuPwb4f8ntnwRBMAcYkHxsThiGHwMEQfD/kvsaytTkuCS+JElq\nshxfbLR0ji9uD+wbBMGUIAheDYJgz+T2zsD8evt9lty2tu2rCYLgnCAIpgVBMO2rr75KQ+mSJK2b\nv4skaS2CwIU+GmmjOmVBELwCdFzDQ8OSx24DDAL2BB4LgqAHsKYWQciaA+IaY3UYhvcA9wD079/f\n6K2sEK7hUyKvUyZlL38XSdJaxGJ2yhppo0JZGIYHru2xIAjOA54ME+9EpwZBEAe2ItEB61pv1y7A\n58nba9suNRkBYYOFPiRJkpoUF/potHSOLz4F7A+QXMgjH1gEPAOcFARBQRAE2wK9gKnAm0CvIAi2\nDYIgn8RiIM+ksT4pci6JL0mSmhwX+mi0dC70MQ4YFwTBe0AlMDjZNXs/CILHSCzgUQ1cEIZhDUAQ\nBBcCLwI5wLgwDN9PY31SZOov9OHPKUmS1KS40EejpS2UhWFYCZy8lseuB65fw/YJwIR01SRJkiQp\nzRxfbLS0Xjxa0rp5nTJJktTkOL7YaIYyKQJep0ySJDVZdsoazVAmZcoaPiRySXxJktTk2ClrNEOZ\nFAF/NEmSpCbLhT4azVAmSZIkKXUcX2w0Q5kUIa9TJkmSmhzHFxvNUCZFwOuUSZKkJstOWaMZxarL\nNQAAIABJREFUyqQIuSS+JElqcmIxzylrJEOZlGE15NBu8ZcA9Ov+H9oFZRFXJEmS9MPiyY7X5MmT\n171jENQbX4xRFg5Md2lZz1AmZciCB18GEqHs2+lbMX92Fwb0eJODcw+ArwxmkiRp01VWVkZVVRUA\nhx9+OGVl63jvEgRMZmUQO4BJlN0zI90lZjVDmZQh856enrwVUBOPMXdmd2KxkBiV8GVplKVJkiSt\nU2lpad3tysrKBvdX8803TOTQlfuTR+kTX6evuCbAUCZlyDbH9EveCsmJxeneZy418YCQfGhfEmVp\nkiRJ61RSUlJ3Oz8/v8H91cyYwdYsTN4JyaeKkuO2Smd5Wc9QJmVI59MPAiCHGjrvPY+uvT7j1Vkl\nlDWbBO2KI65OkiRp7YqLi8nLywNgwoQJFBev473LO+9QQy4AezCNSQOHUXzOLpkoM2sZyqQMixGn\ncutCAF5570DKWxjIJEnSpi8WS0SHQYMGrXvHd95hBn0BOIVHKD6+c7pLy3qGMilDgmDltckK8soB\nWFFZRFFRVBVJkiSlwYwZvMOuAOzKO7DrrhEXtOkzlEkRKCpIhLLvK5tRWBhxMZIkSalSXU3NuzN5\nl8S4Yl9mQN++ERe16TOUSZmSbJSFBBTk2ymTJElN0OzZfFTZhRU0owvz2bJDPnToEHVVmzxDmZQh\n9ccXC/MrAEOZJElqYt55x9HFDWAokyJQmL8CcHxRkiQ1MfUW+XB0cf0ZyqRMqTe+aKdMkiQ1SS7y\nsUEMZVKGNFh90XPKJElSU1RvfNFO2fozlEkRKMxLdsqqiigoiLgYSZKkVPj6axYvWMY8ulHICnrl\nzoUdd4y6qqxgKJMyZWWjjMKCxDll1WEzYn4XSpKkpmDGjLrzyXbmPXJ32gHy8yMuKjv4dlDKsPrn\nlBFzdlGSJDURLvKxwQxlUoY0WBI/L3FOWZhjKJMkSU2Ei3xsMEOZlCkNxhdrQ1mziIqRJElKsVUX\n+TCUrTdDmZRh9ccXY7lepEySJDUB1dXUvDeL99gZcHyxsQxlUobUH18EKK8soKDQb0FJktQEfPgh\ncyq7soJmdGE+W3YsgPbto64qa/iOUIrIiiqvUSZJkpqIeot87Mo7dskayVAmZUqyURYmb3xf0cxQ\nJkmSmgYX+dgohjIpQ1YdX1xRWUShp5RJkqSmYNXl8A1ljWIokzIuEc4cX5QkSU3Gqp0yxxcbxVAm\nZUrDRpnji5IkqWn4+mu+XbCceXSjkBX0zP0Udtwx6qqyiqFMyhDHFyVJUpM0YwbvsgsAO/MeuTvt\nAHl5EReVXQxlUgTC0PFFSZLURHjR6I1mKJMi4viiJElqElZdDt9Q1miGMikKYeD4oiRJahpc5GOj\nGcqkjArr/tfxRUmSlPWqq6l5bxbvsTPg+OKGMpRJGRQkQxkkFvowlEmSpKz24YfMqezKCprRlXm0\n2boI2rWLuqqsYyiTohAGfF/RzPFFSZKU3VZd5MPRxQ1iKJMi4viiJEnKei7ykRKGMikCYej4oiRJ\nagLqLfJhp2zDGcqkSDi+KEmSmgA7ZSlhKJMyyIU+JElSk7FoEd9+/j3z6EYhK+iV9ynssEPUVWUl\nQ5kUgTD0nDJJkpTlZsyo65LtzHvk7LQj5OVFXFR2MpRJUUhePNpQJkmSspajiyljKJMi4jllkiQp\nq7nIR8oYyqQIhDi+KEmSspydspQxlEkZ5EIfkiSpSaiupua9WbzHzoCdso1lKJMyqDaSfTa7C706\nfOj4oiRJyhrxeByAyZMnwwcfMLuqGytoRmsW899WA6Fdu4grzF6GMilD5t/zAnFyAPjrTb/kuv1/\nx0dTyiKuSpIk6YeVlZVRVVUFwOGHH07ZU0/xJMcCsIRWHPDdk5Td826UJWY1Q5mUIXOfeKvudk1N\njM8+7MLiD0ujK0iSJGk9lZaW1t2urKykdOpUpjIguSVGJXmUPvF1JLU1BYYyKUO6/XSPuts5OXG6\nbP8ZW/Upia4gSZKk9VRSUlJ3Oz8/n5JOndiahQAE1JBPFSXHtY2ouuxnKJMyZOszDiOHGgB+PvSv\nXPzCn9jlx8URVyVJkvTDiouLyUteGHrChAkUt2pFa74D4CBeZtJPbqP4nF2iLDGrGcqkDKmpWbn6\nYpeeC3hr7qCIK5IkSVp/sVgiOgwaNAgWL2YxWwDwE56m+OCWUZaW9QxlUobU1Ky8HYZAkBNZLZIk\nSRtlyZK6ULYFi6F164gLym6GMilD6ocywFAmSZKyUhiGDTplW7AYttgi4qqym6FMypBVQ1kY5EZT\niCRJ0gYIgmDlHUNZShnKpAxpEMrCwE6ZJEnKXoaylDKUSRni+KIkSWoyDGUpZSiTMqTBQh9AEPPb\nT5IkZSlDWUr5rlDKkPpL4lfX5JCTE/zAMyRJkjZB5eWUV0AFheRTQWFuDRQVRV1VVnOlASlD6nfK\nampyyHF6UZIkZaFw8WKW1OuSBVu0hsAPmzeGnTIpQxqEsrihTJIkZZe61RdXvUaZo4sbzVAmZUj9\nUBYPY4YySZKUnZYs4VvaAIayVDGUSRlSP5RVV+cayiRJUnZykY+UM5RJGZIIZYmFPuJxO2WSJClL\nOb6YcoYyKUNqaoDkKHZN6DllkiQpS333XV0oa8O3hrIUMJRJGZJYEr/2tqFMkiRlp3DV8cXWrSOu\nKPsZyqQMabj6ouOLkiQpu9Stvug5ZSlnKJMypMHqiy6JL0mSspXnlKWcoUzKkPoLfTi+KEmSspah\nLOUMZVKG2CmTJElNguOLKWcokzKkweqLnlMmSZKylZ2ylDOUSRnSYPXFeA4xv/skSVIWCustiW8o\nSw3fFkoZ4viiJEnKZmtdfdEl8TeaoUzKkAYLfTi+KEmSslS42PHFVDOUSRnS4JwyV1+UJElZqrw8\nTiUFFFBOYawKWrSIuqSsZyiTMqThxaMNZZIkKTstITGuWNclqx1r1AYzlEkZUn+hj7jji5IkKUs5\nuph6hjIpQ+p3yqrtlEmSpCy1WqdMG81QJmVI/XPKXH1RkiRlq8WGspQzlEkZUn/1RccXJUlStqld\nEr9Bp8zl8FMibaEsCILdgiCYHATB20EQTAuCYEByexAEwdggCOYEQTAjCILd6z1ncBAEs5Nfg9NV\nmxSFBgt9uPqiJEnKUo4vpl5uGo99I3BNGIYvBEFwePJ+CXAY0Cv5NRC4ExgYBMGWwB+A/iTaCW8F\nQfBMGIbfprFGKWPqL/ThdcokSVK2MpSlXjrHF0OgVfJ2a+Dz5O1jgIfChMnAFkEQbA0cArwchuE3\nySD2MnBoGuuTMqrBOWWhnTJJkpSdXH0x9dLZKRsCvBgEwSgS4W+v5PbOwPx6+32W3La27VKTUP+c\nsuqaHHLyIi1HkiRpgzTslHWKuJqmYaNCWRAErwAd1/DQMOAA4JIwDJ8IguAE4H7gQFZOcNUXrmP7\nml73HOAcgG222WYDKpcyr/45ZaGrL0pZz99FkjZXS5LDcHbKUmejxhfDMDwwDMOd1/D1NDAYeDK5\n69+BAcnbnwFd6x2mC4nRxrVtX9Pr3hOGYf8wDPu3a9duY/4KUsY0WOjDc8qkrOfvIkmbmzWuvmgo\nS4l0nlP2ObBf8vb+wOzk7WeAU5OrMA4CloRhuBB4ETg4CII2QRC0AQ5ObpOahJoaSP4sc/VFSZKU\ntZbUP6fMJfFTIp2h7Gzg5iAI3gFGkBzxACYAHwNzgHuB8wHCMPwGGA68mfy6NrlNahIadMpCO2Vq\n6Oqrr2bnnXeOuow6ixYtIggCSktLoy5FkrSJ8eLRqZe2UBaG4ethGO4RhuGuYRgODMPwreT2MAzD\nC8Iw3C4Mw13CMJxW7znjwjDsmfx6IF21SVGoqYF4mGiVdeJzWpSXRVyR1qWkpIQLL7wwY8/7IXPn\nziUIAqZNm/bDO0uSlAY1yU+Yv+BTAOawnaEsRdLZKZNUzxZlL1BeVQTAG4/vy6lbnMy7/zSYSZKk\nTV9ZWRnff/89AMs4GSjjBP5O2VNfRFtYE2EokzKk8P236m7X1OSwYHZnvp5ZGl1BEQmCaL/Wx2mn\nncarr77K7bffThAEBEHA3LlzAXjttdcYOHAghYWFdOjQgUsuuYTKysp1Pq+mpoYzzzyTbbfdlqKi\nInr16sWNN95IPB5f73+3bbfdFoA999yTIAgoKSmpe+yBBx6gT58+FBYWsv322zN69OgGxw6CgHvu\nuYef/exnNG/enB49evDII480OP6bb77JHnvsQWFhIf369WPKlCmr1TBz5kyOOOIIWrZsSfv27fn5\nz3/O//73vwb/bkceeSRjxoyhc+fOtGnThtNPP73ulzhAGIbcfPPN9OrVi4KCArp06cKVV14JwP77\n779al/G7776jWbNmPPnkk0iSotNwnL0SKKWSPEqfWxZRRU2LoUzKkPKd9qi7nZNTQ+deC2jbpyS6\ngrRWY8aMobi4mNNPP52FCxeycOFCunbtyoIFCzjssMPo168f06dP5/777+fRRx+tCxVre148Hqdz\n58489thjzJo1i+uvv54RI0bwwAPrP6U9depUACZOnMjChQvrQsq9997LVVddxbXXXsusWbO4+eab\nGTlyJHfccUeD51977bUcc8wxvPPOO5x44omcccYZfPppYvxk+fLlHHHEEfTo0YNp06bxxz/+kaFD\nhzZ4/sKFC/nRj37EzjvvzNSpU3nllVdYtmwZRx99dIMA+K9//Yv33nuPV155hb/97W+MHz+eMWPG\n1D1+1VVXMXz4cK688kref/99/v73v9O1a2Lh3bPPPpu//vWvVFRU1O3/6KOP0qJFC4466qj1/reS\nJKVe/Q8DIR8oIZ8qSo5qGVFFTUwYhln9tccee4RSNrjhhjAcmf+b8GquDl8+4cfh1Re+EXVJkYBo\nv9bXfvvtF15wwQUNtl111VXhdtttF9bU1NRte+CBB8L8/Pxw+fLla33emlx++eXhAQccUHf/D3/4\nQ7jTTjutdf9PPvkkBMI333yzwfauXbuGDz30UINto0ePDnv37l13HwivuOKKuvtVVVVhUVFR+PDD\nD4dhGIZ333132Lp163Dp0qV1+zz88MMhEP7zn/8MwzAMf/e734X7779/g9f55ptvQiCcMmVKGIZh\nOHjw4LBLly5hVVVV3T5nnXVW3d9z6dKlYUFBQXjnnXeu8e9YXl4etm3bNnz00Ufrtg0YMCC87LLL\n1vrvsi7AtNDfRZKUMkVFRSEQ5vN0CGH4IgeG4RdfRF3WJm19fxfZKZMyJAggJ5Y4QXZedTeWFxVH\nXJEaa9asWRQXFxOLrfzRuc8++1BZWcmcOXPW+dy77rqL/v37065dO1q0aMHo0aOZN2/eRtXz1Vdf\nMX/+fM4991xatGhR93XFFVfw0UcfNdi3b9++dbdzc3Np164dX375Zd3fq2/fvrRo0aJun+Lihv99\nvvXWW7z22msNXqe2w1X/tfr06UNubm7d/U6dOtW9zsyZM6moqOCAAw5Y49+noKCAU045hXHjxtXt\nP3XqVM4444xG/9tIklIvJ7l0dGv6ANCXd6HedIM2XO4P7yIpFWKxeuc0Jfs2m6ON+XuXlUFpKZSU\nQHEEmTYMw7oLZ65qbdsB/va3vzFkyBBGjRrFXnvtRatWrbj99tsZP378RtVTOzZ41113sddee61z\n37y8vNXqrX1+uB7/p8TjcY444ghGjRq12mMdOnRI2eucddZZ9O3bl3nz5nH//fdTXFxMnz59fvB5\nkqT0q/05Xkg5AOUUQnl5lCU1GYYyKUOCAAJWviltxBoPSiouzlwYy8/Pr1v6t1afPn147LHHiMfj\ndd2y119/nfz8fLbbbru1Pu/1119n4MCBDRaxWLWTtT71AA2O3aFDBzp37sxHH33Eqaee2qjj1den\nTx/+/Oc/s3z5cpo3bw7A5MmTG+yz++6789hjj9GtW7fVgldjXqegoIBJkybRq1evNe6z0047MXDg\nQO69914eeeQRrr/++g16LUlS+hSS6I6toMhQliKOL0oZEqv/3RYGm22nLFt0796dqVOnMnfuXBYt\nWkQ8Huf888/n888/5/zzz2fWrFk8//zzXHHFFVx44YU0a9Zsrc/bfvvt+c9//sMLL7zA7NmzGT58\nOK+++mqj6mnfvj1FRUW8+OKLfPHFFyxZsgRIXHT6xhtvZPTo0XzwwQe89957PPTQQ9xwww3rfexf\n/OIX5ObmcsYZZ/D+++/z8ssvrxaGLrjgApYsWcKJJ57IlClT+Pjjj3nllVc455xzWLp06Xq9TsuW\nLbn44ou58soreeCBB/joo4+YOnUqd955Z4P9zj77bG688UaWL1/OiSeeuN5/D0lSZtgpSz1DmZQh\nsRgQJJJYiJ2yTd3QoUPJz8+nT58+tGvXjnnz5tG5c2deeOEFpk+fzm677cYZZ5zBz3/+c0aMGLHO\n55177rmccMIJ/OIXv2DPPfdk7ty5XHbZZY2qJzc3l7Fjx3LffffRqVMnjjnmGCAx7jdu3Dgefvhh\ndt11V/bdd1/uueeeuiX010eLFi147rnnmD17NrvvvjtDhw5l5MiRDfbp1KkT//73v4nFYhx66KHs\ntNNOXHDBBRQUFFBQULDer3XDDTdw+eWXM3z4cHr37s1xxx3HZ5991mCfE088kfz8fE444QRatnRV\nL0naVDi+mD7B+sz4b8r69+8fTps2LeoypB80ZgyEVw1hyfdt6HLMp7yzzTjGjo26KmnT8/nnn7PN\nNtvw6quvsvfee2/wcYIgeCsMw/4pLG2t/F0kaXPQvHlzvv/+e/blef7F4Uxif/Z/6Uo46KCoS9tk\nre/vIs8pkzIkFoO4C31Ia1VVVcXChQsZNmwY/fr126hAJklKPTtl6eP4opQhLvQhrdu///1vunXr\nxpQpU7j33nujLkeStBa1C30YylLHTpmUIbEY1OUwF/qQVlNSUrJey+ZLkqKxaqfM1RdTx06ZlCFB\nAEFgp0ySJGW3IscXU85QJmVI/SXxQ88pkyRJWcpzylLPUCZlSP1zygJCO2WSJCmr1I4vFnjx6JQz\nlEkZUv86ZZ5TJkmSslUzVgB2ylLJUCZlSKJTtpKdMkmSlE1W7ZQZylLHUCZlSCy2cqEPzymTJEnZ\nqsjVF1POUCZlSGyV7zY7ZZu2eDzOueeeS9u2bQmCgNLS0rS8TklJCRdeeGFaji1JUiqtsVNWURFl\nSU2GoUzKkCAAai8eHRrKNnUTJkzggQce4Nlnn2XhwoXstddeaXmdJ598khtuuCEtx5Y2xmmnnUYQ\nBARBQG5uLttssw3nnXce3377bd0+3bt3Z9SoUas9d9SoUXTv3r3ufk1NDSNHjqR37940a9aMNm3a\n0L9/f8aOHZuJv4qkFPOcstTz4tFShtQfXwQX+tjUzZkzh6233nqjwlhVVRV5eXnr3GfLLbfc4ONL\n6XbggQfy8MMPU11dzcyZMznjjDNYvHgxjz76aKOOc80113DHHXdw2223MWDAAJYtW8b06dOZN29e\nmiqXlE5ePDr17JRJGdJgoQ87ZRumrAxuuCHxZxqddtppXHLJJcybN48gCOjevTsVFRUMGTKEDh06\nUFhYyKBBg3j99dfrnlNaWkoQBEyYMIEBAwaQn5/Piy++CMDzzz/PwIEDKSoqom3bthx11FGUJ3+J\nrTq+2L17d6677jrOPfdcWrVqRZcuXbjpppsa1Pfhhx+y3377UVhYyA477MCECRNo0aIFDz74YFr/\nXZQCiavIR/fVSAUFBXTs2JEuXbpw8MEHc+KJJ/LSSy81+jjPPPMMv/rVrzjppJPo0aMHffv2ZfDg\nwfzud79r9LEkRceFPtLHTpmUIfWXxA/ZzBf62IA3hymxnv/oY8aMoVu3bowbN44333yTnJwcfvvb\n3/LYY48xbtw4evTowS233MKhhx7K7Nmz2Xrrreuee/nll3PzzTfTs2dPWrZsycSJEznmmGO44oor\neOCBB6iuruall14ivo5UPnr0aK655hp+85vf8MILL3DRRRexzz77UFxcTDwe59hjj6Vjx45MnjyZ\nFStWMGTIECqc6Veaffzxx0ycOPEHu79r0rFjR0pLS/niiy/o0KFDGqqTlEmOL6aeoUzKEDtl2aN1\n69a0bNmSnJwcOnbsyPLly7nzzju57777OOKIIwC46667+Mc//sHtt9/OddddV/fcq6++moMPPrju\n/vDhwzn++OMb7NO3b991vv7BBx9c1z379a9/zdixY5k0aRLFxcW8/PLLfPDBB7z00kt07twZSIS4\nvffeO2V/f6nWxIkTadGiBTU1NXXd3VtuuaXBPsOGDePqq69usK2qqqrBhxW33HILxx9/PFtvvTW9\ne/emuLiYww8/nGOPPZYgqg9pJDVabafM8cXUc3xRypDE6osrOzWbdaes9poAjfl64w0oKoKcnMSf\nb7zR+GNsoI8++oiqqqoGwScnJ4fi4mJmzpzZYN/+/fs3uD99+nQOOOCARr3eqqGtU6dOfPnllwD8\n97//pVOnTnWBDGDPPfckturynlIK/OhHP+Ltt99m6tSp/PrXv+bwww/noosuarDPpZdeyttvv93g\n69JLL22wT58+fXjvvfeYMmUKZ511Fl9//TUnnHACRxxxxDq7xpI2TYWOL6acv8WlDEmc0lG7+mJg\np6yxioth0iQYPjzxZ3Fxxl669pPBNX2iv+q25s2bb/TrrToeFgRB3RvXMAztLGSzDflAov4HEyNG\nbNgHEhv4wUSzZs3o2bMnu+yyC2PHjuX7779n+PDhDfZp27YtPXv2bPDVtm3b1Y4Vi8XYc889ueSS\nSxg/fjwPPvggL7zwAq+99toG/3NKyqza34dFji+mnKFMypBVGxmbdadsQxUXw5VXZjSQAfTs2ZP8\n/PwGC3vU1NRQVlZGnz591vncfv36MWnSpJTV0rt3bxYsWMDnn39et23atGl2GzYHEf33X98f/vAH\nRo4c2eC/vw1V+72zbNmyjT6WpMzy4tGp5zllUobEYhAkxxdDzynLKs2bN+e8887jiiuuYKuttmLb\nbbdl9OjRfPHFF5x//vnrfO6wYcM46qij6NmzJ7/4xS8Iw5CXXnqJc889l2bNmjW6loMOOogddtiB\nwYMHM2rUKFasWMGll15Kbm6uHTSlXUlJCTvttBPXXXcdd9xxx3o/7/jjj2fvvfdmr732omPHjnzy\nySdceeWVtG/fPm3XAJSUequeU2anLHXslEkZ0mBFakNZ1hk5ciQnnHACp59+OrvtthszZsxg4sSJ\nDRYzWJPDDz+c8ePH88ILL9CvXz/2228//vnPf27wOWCxWIzx48dTUVHBgAEDGDx4MMOGDSMIAgoL\nCzfomFJjXHrppdx///18+umn6/2cQw45hOeff56jjz6a7bffnlNOOYVu3brxj3/8w2v1SVnI1RdT\nLwizfIaqf//+4bRp06IuQ/pBzzwDX55+Fgu+6Uqngz7j2cJ7eeaZqKtSU/DOO++w2267MW3aNPbY\nY4+oy9lkBEHwVhiG/X94z43n7yJJm4NYLEYYhnxNK9qyhFYsYUnb7WDRoqhL22St7+8ixxelDKm/\nJL7ji9oY48ePp3nz5vTq1Yu5c+dy6aWXsuuuu7L77rtHXZokqQlzfDF9DGVShtS/eDS40Ic23NKl\nS7n88suZP38+bdq0oaSkhNGjR3tOmSQpIwqpBKCSAuIrKjwfKgUMZVKGJDpltUvi2ynThjv11FM5\n9dRToy5DkrSZCoKAwnAF5RRRHs+jWXU15BorNobBVsqQhus6BHbKJElSVgqKihqOMFZURFxR9jOU\nSRnS8OLRdsokSVL2aLA4YGGhF5BOMUOZlCENOmWh55RJkqQsVVhY1ynzAtKpYSiTMqT+OWUhdsok\nSVKWqhfK7JSlhqFMypBYrN74InbKJElS9mgwvlhQ4PhiirlMipQhDccXA+KGMkmSlI0cX0w5O2VS\nhjRY6APHF5uiRYsWEQQBpaWlkdYxd+5cgiBg2rRpkdYhSWo6Vl3ow/HF1DKUSRniQh9Kh5KSEi68\n8MIG27p27crChQvZbbfdIqpKTcFpp53GkUceucbHunfvzqhRo1bbPmrUKLp37153v6amhpEjR9K7\nd2+aNWtGmzZt6N+/P2PHjk1X2ZLSLAgCV19MA8cXpQxxoQ9lSk5ODh07doy6DIlrrrmGO+64g9tu\nu40BAwawbNkypk+fzrx586IuTVIjra1T5vhiatgpkzIkFoOKqnwA2se+ZNuWZRFXlIW+KoP3b0j8\nmQETJ05k3333pU2bNmy55ZYccsghzJo1q+7xN998kz322IPCwkL69evHlClT6h6Lx+N06dKFP/3p\nTw2O+eGHHxIE/7+9uw+Porr7P/7+bkIQiIJoeKZQitqkGBFBG1AJDxdUUZEaFNQUCq2lqFW5YhW5\n8QkV8abwUyu/yqNWUGkt/opYFUUx0CaoFFAgokSpCBS0d9EbEAPJ+f2xk+2GbB5gNzu78Hld116Z\nPTM7+92T2Zn5zjlz1li3bh0AX331FTfccAOtWrXi5JNPpm/fvtW6HRYXF9O/f3+aNWtG8+bNGTBg\nADt37mT06NG8/fbbPPHEE5gZZsa2bdsidl8sLCzkggsu4KSTTqJ169bcdtttlJWVhebn5uYyfvx4\n7rrrLk4//XRatWpFQUEBFbp6kDCKimDq1ODfZLF06VLGjRvHiBEj6NKlC9nZ2YwaNYrJkyf7HZqI\nHCPnHEX79/O/pAPwAd2UlMWAWspE4qTsz6+wc28HADav6sZ9v76eD95ayNn9cnyOzAfPmj/ve+3R\n9Rndv38/t956K9nZ2XzzzTc88MADXH755WzevJlDhw4xZMgQ+vbty9NPP82OHTu49dZbQ68NBAKM\nHDmSRYsWcfPNN4fKFy1aRFZWFueeey7OOYYMGULz5s1ZtmwZLVu25Omnn6Z///5s2bKFtm3bsmHD\nBvr160d+fj4zZsygcePGFBYWcvjwYR599FE++ugjvv/97/PQQw8BkJGRwfbt26t8jh2ZIIUdAAAc\ne0lEQVQ7dnDJJZeQn5/PU089RWlpKT/72c8IBAL85je/qRLbLbfcwt/+9jfWr1/Ptddey3nnncfI\nkSOPpbalBubT5u9Hl+k2bdqwcuVKdu/eTevWreMfgIjETFHYFaF+q/7GIZoB8DATueQvvyfnKr8i\nOz4oKROJk30r1+Jc8GysvDzAjo/bU9FuJZyISVmSuOqqqkeYBQsWcMopp/DOO++wefNmysrKWLBg\nAenp6XTr1o1JkyaRn58fWj4/P5/p06ezdetWunbtCsCzzz7LmDFjAHjrrbdYv349X3zxBU2aNAFg\nypQpvPTSSzzzzDP8+te/5pFHHuGcc85h9uzZofVmZmaGptPS0mjatGmt3RVnzZpF27ZtmTVrFoFA\ngMzMTB5++GF+8YtfMGXKFJo2bQpAVlYW999/PwBnnnkmc+bMYcWKFUrKJKJJkyZx7733Vik7dOgQ\nbdu2DT2fMWMGeXl5tG3blszMTHJycrj00ksZNmxY8L4UEUkahYWFoekyV4FjFXAR5aSwck0TdDYT\nHSVlInFySv/zsHeLcc4IBCpof8YODmTl+h2WP46yxQoIdll8cwBUlEEgDfqvgIyGPQSUlpYyefJk\n1qxZwxdffEFFRQUVFRV89tlnlJSUkJ2dTXp6emj5nJyq8WRnZ3P22Wfz7LPPcvfdd7NmzRpKS0u5\n9tprAVi7di0HDhwgIyOjyusOHjxIaWkpAOvWrWPYsGFRfY6SkhJycnIIhI02c+GFF1JWVsbWrVvJ\nzs4OxRuuXbt27NmzJ6r3luqOpcWqqAgGDICyMkhLgxUrIMfnM6AJEyYwduzYKmXz5s3jueeeCz3P\nyspi48aNrF27ltWrV1NYWMjVV1/NoEGDWLZsWZVtUkQS28UXXxyaTgsEOFxxEeVACuXk9vjav8CO\nE0rKROKk8ZWX0Gn+c2zb8z3OOP8j/uudhTz3oK4r1VtGTjAR27MSWuU2eEIGcPnll9O+fXuefPJJ\n2rdvT2pqKllZWZSVlVW94bkW1113HfPnz+fuu+9m0aJFXHTRRXTq1AkI3nfWunVrVq1aVe11p5xy\nCkC936c2zrkaWyXCyxs1alRtnu4pSww5OcFEbOVKyM31PyEDOO2000ItwOFlRwoEAvTq1YtevXpx\n2223sXDhQvLz8yksLCQ3NzdO0YpItMIvPL415BJmvrSTPwLjmUXOhU39C+w4oaRMJE7MIL3ZfgB2\nHmxP6eEEOKtKNhk5cUnGAP71r39RUlLCE088Qb9+/QD4+9//zuHDh4FgC8DTTz/N/v37adYs2K++\nuLi42nquu+467rrrLoqLi1m8eDEPPPBAaF6PHj3YvXs3gUCALl26RIyjR48evPnmmzXGmZaWRnl5\nea2fJSsriz/84Q9UVFSEWiZWr15NWloa3/ve92p9rSSOnJzESMailZWVBcC+fft8jkREjkblRTwz\nI6ddO/7IDgC+w2dQfoafoR0X1G9AJI4CKcFWh/LDKT5HInU59dRTOf3005kzZw5bt27l7bffZty4\ncaSmBq9lXXvttaSmpjJmzBg2bdrE66+/zoMPPlhtPR06dODiiy9m3LhxfPXVVwwfPjw0b+DAgfTp\n04ehQ4fyyiuv8Omnn1JUVMQ999wTaj27/fbbWbduHTfccAMbNmxgy5YtzJ07NzSkeOfOnXnnnXfY\ntm0bX375ZcSWrfHjx7Nz507Gjx9PSUkJL7/8MnfeeSc33XRT6H4ykUi+/vpr1q9fX+Wxbdu2er8+\nLy+PmTNnsmbNGv7xj3+wcuVKbrzxRlq1akXv3r0bLnARaTDOOQgECBA83lQQ0O/8xICSMpE4CqQE\nWzTKywP68egEFwgEWLx4Me+//z7dunXjxhtvZMqUKTRu3BiA9PR0li1bxscff0yPHj0oKChg2rRp\nEdeVn5/Phg0bGDJkCC1atAiVmxl/+ctf6N+/Pz//+c8566yzuPrqq9myZQvt2rUDoHv37rzxxht8\n+OGH/PCHP+SCCy7g+eefD3U1LCgoIC0tjaysLDIyMiL+/lP79u155ZVXWLduHd27d2fMmDGMHDky\nNGKjSE1WrVrFueeeW+VRUFBQ79cPHjyYl19+mSuuuIIzzzyT/Px8OnXqxJtvvknLli0bMHIRibUq\n3eBTUqomZXX02JC6WSzuV/BTz5493ZG/6SOSiN59F3aPHsLazefT6KxvefHkh3j3Xb+jEjl+mdla\n51zPeLyXjkUicryrqKggJSXY08fdcgt3PtqGadzJVO7kzpltIOxnYeQ/6nssUkuZSByleN0XK9RS\nJiIiIkmkSktZWPfFclLUUhYDSspE4sQMUlKDO62Kcn31REREJEmlpJCCd06j7osxoTNDkTgKqKVM\nREREklBNLWUa6CM2lJSJxFFKanA4dbWUiYiISNLSQB8xpzNDkTgJdl+sbCmL/EO+IiIiIonOqaUs\n5pSUicRRwLunzFWo+6KIiIgkqSOTMrWURU1JmUicmEFKo8qkTC1lIiIikpycRl+MOSVlInFU2X3R\nlZtaykRERCSphAb7CASqjr6o7otRU1ImEifhQ+KrpUxERESSldNAHzGnpEwkjkJJmVNSlowuu+wy\nRo8eDUBubi433XRTaN6BAwfIy8ujefPmmBnbtm2LWBatp556ivT09KjXIyIicrRCLWVHJmVqKYua\nkjKROAo0Cg6JTwXqvpjklixZwtSpU0PP58+fT2FhIatXr2bXrl107NgxYlm0rrnmGj755JOo1yNS\nl9GjR2NmmBmpqal85zvf4Ze//CX//ve/qyzXuXNnpk+fXu3106dPp3PnzqHn5eXlTJs2jczMTJo2\nbcqpp55Kz549eeyxx2IS62WXXVbj/ESIUeR44szUUhZjqX4HIHKiCA704d1Tpu6LSa9ly5ZVnm/d\nupXMzEzOPvvsWsui1aRJE5o0aRKz9YnUZuDAgTzzzDMcPnyYzZs3M2bMGPbu3ctzzz131Ou67777\nmDVrFr/97W85//zz2bdvH+vWreOzzz5rgMiPTTLEKOKnSC1lGugjNtRSJhJHlaMvqqXs2Gwv2s6q\nqavYXrS9wd/rwIEDjB49mvT0dFq3bs1DDz1UZX5498Xc3FweffRRCgsLMTNyc3MjlkHkK/ZHdoVc\nsmQJ2dnZNGnShJYtW9K3b192794NRO6++OSTT9K1a1fS0tLo2rUrc+bMqTLfzJg9ezbDhw+nWbNm\ndOnShYULF8akniR+ioqKmDp1KkVFRXF7z8aNG9OmTRs6dOjAoEGDuOaaa1i+fPkxrWvp0qWMGzeO\nESNG0KVLF7Kzsxk1ahSTJ0+OcdTHLhliFEkE1e4pU/fFqKmlTCROzCC1svviCX5P2X12ny/ve4+7\np97LFhQU8Prrr/OnP/2J9u3bc99991FYWMiPf/zjassuWbKEgoICPvzwQ5YsWUJaWlpoHUeW1eWf\n//wnI0aMYOrUqVx11VXs27eP4uLiGpd/8cUXuemmm5g5cyaDBg3itddeY/z48bRp04bLL788tNz9\n99/Pww8/zNSpU5k3bx5jxozhoosuolOnTvWuE4mN0JXmOHNRXgn65JNPePXVV2nUqNExvb5Nmzas\nXLmS3bt307p166hiaSjJEKOIn2ocfVEtZVFTUiYSR4FQS5mayRLZvn37mDdvHvPnz2fw4MEALFiw\ngA4dOkRcvmXLljRt2pS0tDTatGkTKo9UVpedO3dy6NAh8vLyQglTt27dalx++vTp5Ofnh1razjzz\nTNauXcu0adOqJGX5+flcf/31AEyZMoVHH32UVatWKSmTWr366qukp6dTXl7OwYMHAZgxY0a15SZN\nmsS9995bpezQoUO0bds29HzGjBnk5eXRtm1bMjMzycnJ4dJLL2XYsGFxSVSTIUaRpKGWsphTUiYS\nR5X3lMGJ3X3xaFqsKm0v2s7vB/ye8rJyUtJS+MmKn9AxJ/qBMyIpLS2lrKyMnJycUFl6enpM7w2r\nyTnnnMPAgQPp1q0bgwYNYuDAgeTl5ZGRkRFx+ZKSEsaMGVOl7MILL2Tp0qVVyrKzs0PTqampZGRk\nsGfPnth/AKnTsbRYFRUVMWDAAMrKykhLS2PFihVVts+GcvHFFzN79my++eYb5syZQ2lpKb/61a+q\nLTdhwgTGjh1bpWzevHlV7j3Lyspi48aNrF27ltWrV1NYWMjVV1/NoEGDWLZsGYFA9TsqLrnkElat\nWgVAp06d2LRp0zF/loaKUeREpIE+Yk9JmUicBLsvBndapgtKR61jTkd+suInbFu5jc65nRssIYPo\nu3nVJhAIVFv/oUOHQtMpKSksX76c4uJili9fzrx585g4cSJvv/0255xzTsR1RrqCf2TZkV3OzIwK\nXdlMGjk5OaxYsYKVK1eSm5sbl4QMgq29Xbt2BeCxxx6jX79+TJkypVqL02mnnRZaLrzsSIFAgF69\netGrVy9uu+02Fi5cSH5+PoWFhaH7LsPNnTuXb775Bqi+DR+thopR5ERS45D4Ssqipks+InGU0rjy\nnjJ3QreUHauOOR25aOJFDZqQAXTt2pVGjRpVuZdr//79bNy4Mep1Z2RksGvXrtDzgwcP8uGHH1ZZ\nxszIycnhnnvu4d1336Vdu3YsXrw44voyMzNZvXp1lbLVq1eTlZUVdaySWHJycpg4cWLcErJI7rnn\nHqZNm8bOnTtjsr7K7XTfvn0R57dv356uXbvStWtX37ra1hWjyInIBQJVR1/URb6oqaVMJE6CQ+J7\nLWUoI0tk6enpjB07ljvuuIOMjAzatWvH/fffT3kMrgT279+f+fPnc8UVV5CRkcGDDz5YpaWsuLiY\nN954g8GDB9O6dWvWrVvH9u3ba0yybr/9doYPH855553HoEGDePXVV1m0aBFLliyJOlaRI+Xm5vKD\nH/yABx54gFmzZh3Va/Py8ujTpw+9e/emTZs2fPrpp0ycOJFWrVrRu3fvqGP7+uuvWb9+fZWyFi1a\nVPkdMr9jFEl2Guij4SgpE4mjUFKmnCzhTZ8+nf379zNs2DCaNm3KzTffzP79+6Ne78SJE9m2bRtD\nhw4lPT2dSZMmVWl1aN68OX/96195/PHH2bt3Lx07dmTy5MmhQTqOdOWVV/L4448zffp0br31Vjp1\n6sSsWbOqDPIhEksTJkzgpz/9KXfcccdRtV4NHjyYxYsX8/DDD7N3715atWpFnz59mDt3brXf/TsW\nq1at4txzz61SdtVVV/HCCy8kTIwix4vwljIN9BEb1pD3TsRDz5493Xvvved3GCJ1+uAD6LT8ZGYW\nFPAtaSz9wURi0BtORGpgZmudcz3j8V46FonIieCkk07i22+/5ZsFC1jy0+Vcx7OM5FmeHfESHMOP\nyp8I6nss0j1lInFiBilpwZayyqtLIiIiIsmm2o9Hq/ti1JSUicRRZVJmaKAPERERSS7h95Sp+2Js\nKSkTiZPwljJTS5mIiIgkqWqjL6qlLGpKykTixVWQ6v14dAruxP71aBEREUk64b9TVmX0RbWURU1J\nmUicmDuEBRwW8HZcTjswERERSUJHdl9US1nUlJSJxIkR/C2qlBRvsA8lZSIiIpKEqg2Jr6QsakrK\nROLEXNWkzCq0AxMREZHkEd59UQN9xJaSMpE4CXgtZYGU4I4r4JSUiYiISPJxZmopizElZSJxYhwG\nwlrK1H1RREREkkiklrJyUtRSFgNKykTixeu+GPAG+jC1lImIiEgScmZVR19US1nUlJSJxEnAaaAP\nERERSV413lOmpCxqUSVlZjbczDaZWYWZ9Txi3kQz22pmW8xscFj5j7yyrWZ2Z1j5d81sjZl9bGaL\nzSwtmthEEo0G+hAREZHjQbXRF9V9MWrRtpRtBH4MFIYXmlkWMAL4AfAjYJaZpZhZCvAEcAmQBYz0\nlgWYBsx0zp0B/BsYG2VsIgklNNCH130xgJIyERERSR6hljINiR9zUSVlzrkS59yWCLOGAs875751\nzn0KbAXO9x5bnXOfOOfKgOeBoRb8D/cHXvBe/zRwZTSxiSQeb6CPQGVLma4qiYiISPIJbykrJ0VJ\nWQykNtB62wPFYc8/98oAth9RfgFwGrDXOXc4wvLVmNkNwA3e031mFikxjIfTgS99eu9Ep7qp0exg\n3Wy/l8oLThKi7aZ2qp+aRaqbTg35hj4fi5J9W1D8/lL8/kr6+Fv07+/Fb/wNsLWQRCc18a7/eh2L\n6kzKzOwNoE2EWZOcc3+u6WURyhyRW+ZcLctH5JybDcyuaX68mNl7zrmedS954lHd1Ex1UzPVTe1U\nPzXzo278PBYl+7ag+P2l+P2l+P2VqPHXmZQ55wYew3o/BzqGPe8A7PSmI5V/CbQws1SvtSx8eRER\nERERkeNWQw2JvxQYYWaNzey7wBnAO8C7wBneSItpBAcDWeqcc8BbQJ73+lFATa1wIiIiIiIix41o\nh8QfZmafAznAy2b2GoBzbhPwB2Az8Cpwo3Ou3GsFuwl4DSgB/uAtC3AHMMHMthK8x2xeNLHFie9d\nKBOY6qZmqpuaqW5qp/qp2YlWN8n+eRW/vxS/vxS/vxIyfgs2UomIiIiIiIgfGqr7ooiIiIiIiNSD\nkjIREREREREfKSmLATMrMDNnZqf7HUsiMbP/NrMPzex9M3vRzFr4HZPfzOxHZrbFzLaa2Z1+x5Mo\nzKyjmb1lZiVmtsnMbvE7pkRjZilmts7MlvkdS6IxsxZm9oK3vykxsxy/Y4oFMxvufR8qzKzG4ZvN\nbJuZfWBm683svbDylmb2upl97P09NT6Rh96/zvhr++6b2b1mtsP7XOvN7NL4RX9U9R9xv+4NarbG\nq//F3gBncVOf/7+Z9Qur3/VmdtDMrvTmPWVmn4bN655o8XvLlYfFuDSsPBnqv7uZFXnb2ftmdk3Y\nPF/qv67zFG8Qv8Xe/DVm1jls3kSvfIuZDY5HvEfEVlfsE8xss1fXK8ysU9i8iNtRXDnn9IjiQXCI\n/9eAfwCn+x1PIj2AQUCqNz0NmOZ3TD7XRwpQCnQB0oANQJbfcSXCA2gL9PCmTwY+Ut1Uq6MJwLPA\nMr9jSbQH8DTwM286DWjhd0wx+lyZwFnASqBnLctti3T8AR4B7vSm74z3Prg+8df23QfuBQoSuf5r\n268THPBshDf9O+CXcY7/qP7/QEvgf4Cm3vOngDwf679e8QP7aihP+PoHzgTO8KbbAbsq919+1H99\nzlOA8cDvvOkRwGJvOstbvjHwXW89KQkWe7+w7fuXlbHXth3F86GWsujNBH5NLT92faJyzi13wRE3\nAYoJ/v7ciex8YKtz7hPnXBnwPDDU55gSgnNul3Pu7970/xIcnbW9v1ElDjPrAAwB5vodS6Ixs1OA\ni/FG7HXOlTnn9vobVWw450qcc1uiWMVQggkr3t8ro4+q/uoTfyJ/9+tZ/xH362ZmQH/gBW+5uNc/\nR///zwNecc4daNCo6u+Yt99kqX/n3EfOuY+96Z3AHiAjbhFWV5/zlPDP9QIwwKvvocDzzrlvnXOf\nAlu99cVLnbE7594K274T7rxUSVkUzOwKYIdzboPfsSSBMcArfgfhs/bA9rDnn5MgJx+JxOsKcS6w\nxt9IEsr/IXjxp8LvQBJQF+ALYIEFu3fONbNmfgcVZw5YbmZrzeyGsPLWzrldEEx+gFa+RFdPNXz3\nb/K6Gs2Pd/fLeqppv34asDfswqQf+/uj/f+PAJ47ouxBr/5nmlnjhgiyFvWN/yQze8/Miiu7XpKE\n9W9m5xNs4SkNK453/dfnPCW0jFe/XxGsb7/PcY72/cdS9bw00nYUV6l+vGkyMbM3gDYRZk0C7iLY\nRe+EVVv9OOf+7C0zCTgMLIpnbAnIIpSphTWMmaUDfwJudc597Xc8icDMLgP2OOfWmlmu3/EkoFSg\nB3Czc26NmT1KsKvQZH/Dqp/67EProY9zbqeZtQJeN7MPnXOFsYuyZjGKv6bv/v8FphDcT04BfkPw\nAl/MxCD+mvbrcdnf13GOcjTraQucTfB2jEoTgX8STBRmE/w92fuPLdIa3zcW8X/H2/67AG+a2QdA\npONHotf/M8Ao51zlxbcGr/9IoUQoO7LefN3ma1Hv9zez64GeQN+w4mrbkXOuNNLrG4qSsjo45wZG\nKjezswn2md0QbLWlA/B3MzvfOffPOIboq5rqp5KZjQIuAwY4r9PuCexzgvcgVuoA7PQploRjZo0I\nnpQtcs4t8TueBNIHuMKCgxycBJxiZgudc9f7HFei+Bz43DlX2bryAsGkLCnUtQ+t5zp2en/3mNmL\nBLvxFAK7zaytc26Xd9K3J9r3ivDeUcdf03ffObc7bJk5QMwHuYlB/DXt178EWphZqtea0CD7+9ri\nN7Oj+f9fDbzonDsUtu5d3uS3ZrYAKIhJ0GFiEX/Y9v+Jma0k2Nr6J5Kk/r0u2C8D/+WcKw5bd4PX\nfwT1OU+pXOZzM0sFmhO8F9Hvc5x6vb+ZDSSYNPd1zn1bWV7DdhTXpEzdF4+Rc+4D51wr51xn51xn\nghtDjxMpIauLmf2I4JWdKxKoj7qf3gXOsOCIUGkEu4r4M8JPgvH6o88DSpxzM/yOJ5E45yY65zp4\n+5kRwJtKyP7D2+duN7OzvKIBwGYfQ4orM2tmZidXThPsvbHRm70UGOVNjwLq3XIVL7V9970T2UrD\n+M/nSiQR9+veRci3CN6nBf7U/9H8/0dyRNfFyvr3/kdXEv/6rzN+Mzu1slufBUfA7gNsTpb697aZ\nF4HfO+f+eMQ8P+q/Pucp4Z8rj+AxyXnlIyw4OuN3gTOAd+IQc6U6Yzezc4EnCZ6X7gkrj7gdxS3y\nSscyOogeEUd92YZGXzyyTrYS7N+73nv8zu+Y/H4AlxIcXayUYPcY32NKhAdwIcFuBu+HbS+X+h1X\noj2AXDT6YqR66Q68520//w841e+YYvS5hhG84PctsBt4zStvB/zFm+5CcJSxDcCm8P0Kwfs8VgAf\ne39bJmD8NX73CXbn+sCbtxRom2jxe88j7te9/8073rHwj0DjOMcf8f9PsNvW3LDlOgM7gMARr3/T\nq/+NwEIgPdHiB3p7MW7w/o5NpvoHrgcOhW3764HuftZ/pO2ZYLfJK7zpk7z63OrVb5ew107yXrcF\nuCSe9V3P2N/wvsuVdb20ru0ong/zghEREREREREfqPuiiIiIiIiIj5SUiYiIiIiI+EhJmYiIiIiI\niI+UlImIiIiIiPhISZmIiMSVmc03sz1mFpMhns3sETPbZGYlZvaYN4S0iIhIjRLtWKSkTERE4u0p\n4EexWJGZ9Sb4mzLZQDegF9A3FusWEZHj2lMk0LFISZmIiMSVc64Q+J/wMjP7npm9amZrzWyVmX2/\nvqsj+Ls5aUBjoBHB36ERERGpUaIdi5SUiYhIIpgN3OycOw8oAGbV50XOuSLgLWCX93jNOVfSYFGK\niMjxzLdjUepRBioiIhJTZpYO9Ab+GNYFv7E378fA/RFetsM5N9jMugKZQAev/HUzu9i7AioiIlIv\nfh+LlJSJiIjfAsBe51z3I2c455YAS2p57TCg2Dm3D8DMXgF+CCgpExGRo+HrsUjdF0VExFfOua+B\nT81sOIAFnVPPl38G9DWzVDNrRPDGanVfFBGRo+L3sUhJmYiIxJWZPQcUAWeZ2edmNha4DhhrZhuA\nTcDQeq7uBaAU+ADYAGxwzr3UAGGLiMhxJNGOReacO5rlRUREREREJIbUUiYiIiIiIuIjJWUiIiIi\nIiI+UlImIiIiIiLiIyVlIiIiIiIiPlJSJiIiIiIi4iMlZSIiIiIiIj5SUiYiIiIiIuKj/w8QqlY5\n26qA6wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aae5ee35b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = 1\n",
    "j = 10\n",
    "i = 10\n",
    "\n",
    "f, axes = plt.subplots(1, 2, sharey=True, figsize=(12,7))\n",
    "f.tight_layout()\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(tendSln[t,:,j,i],tendSln.Z, lw=4, color='blue', marker='.',label='total tendency')\n",
    "plt.plot(forcSln[t,:,j,i],forcSln.Z, lw=2, color='red', marker='.',label='forcing')\n",
    "plt.plot(adv_ConvSln[t,:,j,i],adv_ConvSln.Z, lw=2, color='orange', marker='.',label='advection')\n",
    "plt.plot(dif_ConvSln[t,:,j,i],dif_ConvSln.Z, lw=2, color='purple', marker='.',label='diffusion')\n",
    "plt.legend(loc='lower right',frameon=False,fontsize=14)\n",
    "plt.ylim([-1000,0])\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.axvline(x=0, ymin=0, ymax=1, linewidth=0.5, color = 'k')\n",
    "plt.plot(totalSln[t,:,j,i],totalSln.Z, lw=4, color='red', marker='.',label='RHS')\n",
    "plt.plot(tendSln[t,:,j,i],tendSln.Z, lw=2, color='blue', marker='.',label='LHS')\n",
    "plt.plot(totalSln[t,:,j,i]-tendSln[t,:,j,i],tendSln.Z, lw=2, color='k', marker='.',label='RHS - LHS')\n",
    "plt.setp(plt.gca(), 'yticklabels',[])\n",
    "plt.legend(loc='lower left',frameon=False,fontsize=14)\n",
    "plt.ylim([-1000,0])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "    "
   ]
  }
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